Super_Resolution
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LatentFlowSR: High-Fidelity Audio Super-Resolution via Noise-Robust Latent Flow Matching
2026-04-10 10:18:16Fei Liu, Yang Ai, Hui-Peng Du, Yu-Fei Shi, Zhen-Hua LingAbstract
Audio super-resolution aims to recover missing high-frequency details from bandwidth-limited low-resolution audio, thereby improving the naturalness and perceptual quality of the reconstructed signal. However, most existing methods directly operate in the waveform or time-frequency domain, which not only involves high-dimensional generation spaces but is also largely limited to speech tasks, leaving substantial room for improvement on more complex audio types such as sound effects and music. To mitigate these limitations, we introduce LatentFlowSR, a new audio super-resolution approach that leverages conditional flow matching (CFM) within a latent representation space. Specifically, we first train a noise-robust autoencoder, which encodes low-resolution audio into a continuous latent space. Conditioned on the low-resolution latent representation, a CFM mechanism progressively generates the corresponding high-resolution latent representation from a Gaussian prior with a one-step ordinary differential equation (ODE) solver. The resulting high-resolution latent representation is then decoded by the pretrained autoencoder to reconstruct the high-resolution audio. Experimental results demonstrate that LatentFlowSR consistently outperforms baseline methods across various audio types and super-resolution settings. These results indicate that the proposed method possesses strong high-frequency reconstruction capability and robust generalization performance, providing compelling evidence for the effectiveness of latent-space modeling in audio super-resolution. All relevant code will be made publicly available upon completion of the paper review process.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09188
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SAT: Selective Aggregation Transformer for Image Super-Resolution
2026-04-09 09:02:58Dinh Phu Tran, Thao Do, Saad Wazir, Seongah Kim, Seon Kwon Kim, Daeyoung KimAbstract
Transformer-based approaches have revolutionized image super-resolution by modeling long-range dependencies. However, the quadratic computational complexity of vanilla self-attention mechanisms poses significant challenges, often leading to compromises between efficiency and global context exploitation. Recent window-based attention methods mitigate this by localizing computations, but they often yield restricted receptive fields. To mitigate these limitations, we propose Selective Aggregation Transformer (SAT). This novel transformer efficiently captures long-range dependencies, leading to an enlarged model receptive field by selectively aggregating key-value matrices (reducing the number of tokens by 97\%) via our Density-driven Token Aggregation algorithm while maintaining the full resolution of the query matrix. This design significantly reduces computational costs, resulting in lower complexity and enabling scalable global interactions without compromising reconstruction fidelity. SAT identifies and represents each cluster with a single aggregation token, utilizing density and isolation metrics to ensure that critical high-frequency details are preserved. Experimental results demonstrate that SAT outperforms the state-of-the-art method PFT by up to 0.22dB, while the total number of FLOPs can be reduced by up to 27\%.
Abstract (translated)
URL
https://arxiv.org/abs/2604.07994
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IQ-LUT: interpolated and quantized LUT for efficient image super-resolution
2026-04-08 12:17:32Yuxuan Zhang, Zhikai Dong, Xinning Chai, Xiangyun Zhou, Yi Xu, Zhengxue Cheng, Li SongAbstract
Lookup table (LUT) methods demonstrate considerable potential in accelerating image super-resolution inference. However, pursuing higher image quality through larger receptive fields and bit-depth triggers exponential growth in the LUT's index space, creating a storage bottleneck that limits deployment on resource-constrained devices. We introduce IQ-LUT, which achieves a reduction in LUT size while simultaneously enhancing super-resolution quality. First, we integrate interpolation and quantization into the single-input, multiple-output ECNN, which dramatically reduces the index space and thereby the overall LUT size. Second, the integration of residual learning mitigates the dependence on LUT bit-depth, which facilitates training stability and prioritizes the reconstruction of fine-grained details for superior visual quality. Finally, guided by knowledge distillation, our non-uniform quantization process optimizes the quantization levels, thereby reducing storage while also compensating for quantization loss. Extensive benchmarking demonstrates our approach substantially reduces storage costs (by up to 50x compared to ECNN) while achieving superior super-resolution quality.
Abstract (translated)
URL
https://arxiv.org/abs/2604.07000
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Enhanced Self-Supervised Multi-Image Super-Resolution for Camera Array Images
2026-04-08 08:32:58Yating Chen, Feng Huang, Xianyu Wu, Jing Wu, Ying ShenAbstract
Conventional multi-image super-resolution (MISR) methods, such as burst and video SR, rely on sequential frames from a single camera. Consequently, they suffer from complex image degradation and severe occlusion, increasing the difficulty of accurate image restoration. In contrast, multi-aperture camera-array imaging captures spatially distributed views with sampling offsets forming a stable disk-like distribution, which enhances the non-redundancy of observed data. Existing MISR algorithms fail to fully exploit these unique properties. Supervised MISR methods tend to overfit the degradation patterns in training data, and current self-supervised learning (SSL) techniques struggle to recover fine-grained details. To address these issues, this paper thoroughly investigates the strengths, limitations and applicability boundaries of multi-image-to-single-image (Multi-to-Single) and multi-image-to-multi-image (Multi-to-Multi) SSL methods. We propose the Multi-to-Single-Guided Multi-to-Multi SSL framework that combines the advantages of Multi-to-Single and Multi-to-Multi to generate visually appealing and high-fidelity images rich in texture details. The Multi-to-Single-Guided Multi-to-Multi SSL framework provides a new paradigm for integrating deep neural network with classical physics-based variational methods. To enhance the ability of MISR network to recover high-frequency details from aliased artifacts, this paper proposes a novel camera-array SR network called dual Transformer suitable for SSL. Experiments on synthetic and real-world datasets demonstrate the superiority of the proposed method.
Abstract (translated)
URL
https://arxiv.org/abs/2604.06816
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From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks
2026-04-08 07:13:56Carlos Schmidt, Simon Rei{\ss}Abstract
Visual in-context learning models are designed to adapt to new tasks by leveraging a set of example input-output pairs, enabling rapid generalization without task-specific fine-tuning. However, these models operate in a fundamentally static paradigm: while they can adapt to new tasks, they lack any mechanism to incorporate user-provided guidance signals such as scribbles, clicks, or bounding boxes to steer or refine the prediction process. This limitation is particularly restrictive in real-world applications, where users want to actively guide model predictions, e.g., by highlighting the target object for segmentation, indicating a region which should be visually altered, or isolating a specific person in a complex scene to run targeted pose estimation. In this work, we propose a simple method to transform static visual in-context learners, particularly the DeLVM approach, into highly controllable, user-driven systems, i.e., Interactive DeLVM, enabling seamless interaction through natural visual cues such as scribbles, clicks, or drawing boxes. Specifically, by encoding interactions directly into the example input-output pairs, we keep the philosophy of visual in-context learning intact: enabling users to prompt models with unseen interactions without fine-tuning and empowering them to dynamically steer model predictions with personalized interactions. Our experiments demonstrate that SOTA visual in-context learning models fail to effectively leverage interaction cues, often ignoring user guidance entirely. In contrast, our method excels in controllable, user-guided scenarios, achieving improvements of $+7.95%$ IoU for interactive segmentation, $+2.46$ PSNR for directed super-resolution, and $-3.14%$ LPIPS for interactive object removal. With this, our work bridges the gap between rigid static task adaptation and fluid interactivity for user-centric visual in-context learning.
Abstract (translated)
URL
https://arxiv.org/abs/2604.06748
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Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
2026-04-07 09:54:50Prashant Kumar, Rajesh RanjanAbstract
Fluid flows are governed by the nonlinear Navier-Stokes equations, which can manifest multiscale dynamics even from predictable initial conditions. Predicting such phenomena remains a formidable challenge in scientific machine learning, particularly regarding convergence speed, data requirements, and solution accuracy. In complex fluid flows, these challenges are exacerbated by long-range spatial dependencies arising from distant boundary conditions, which typically necessitate extensive supervision data to achieve acceptable results. We propose the Domain-Decomposed and Shifted Physics-Informed Neural Network (DDS-PINN), a framework designed to resolve such multiscale interactions with minimal supervision. By utilizing localized networks with a unified global loss, DDS-PINN captures global dependencies while maintaining local precision. The robustness of the approach is demonstrated across a suite of benchmarks, including a multiscale linear differential equation, the nonlinear Burgers' equation, and data-free Navier-Stokes simulations of flat-plate boundary layers. Finally, DDS-PINN is applied to the computationally challenging backward-facing step (BFS) problem; for laminar regimes (Re = 100), the model yields results comparable to computational fluid dynamics (CFD) without the need for any data, accurately predicting boundary layer thickness, separation, and reattachment lengths. For turbulent BFS flow at Re = 10,000, the framework achieves convergence to O(10^-4) using only 500 random supervision points (< 0.3 % of the total domain), outperforming established methods like Residual-based Attention-PINN in accuracy. This approach demonstrates strong potential for the super-resolution of complex turbulent flows from sparse experimental measurements.
Abstract (translated)
URL
https://arxiv.org/abs/2604.05652
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MVis-Fold: A Three-Dimensional Microvascular Structure Inference Model for Super-Resolution Ultrasound
2026-04-06 06:59:20Jincao Yao (Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China), Ke Zhang (Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China), Yahan Zhou (Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China), Jiafei Shen (Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China), Jie Liu (Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China), Mudassar Ali (College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China), Bojian Feng (Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China), Jiye Chen (Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China), Jinlong Fan (Research Center of Interventional Medicine and Engineering, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China), Ping Liang (Department of Ultrasound, Chinese PLA General Hospital, Chinese PLA Medical School, Beijing, China), Dong Xu (Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China)Abstract
Super-resolution ultrasound (SRUS) technology has overcome the resolution limitations of conventional ultrasound, enabling micrometer-scale imaging of microvasculature. However, due to the nature of imaging principles, three-dimensional reconstruction of microvasculature from SRUS remains an open challenge. We developed microvascular visualization fold (MVis-Fold), an innovative three-dimensional microvascular reconstruction model that integrates a cross-scale network architecture. This model can perform high-fidelity inference and reconstruction of three-dimensional microvascular networks from two-dimensional SRUS images. It precisely calculates key parameters in three-dimensional space that traditional two-dimensional SRUS cannot readily obtain. We validated the model's accuracy and reliability in three-dimensional microvascular reconstruction of solid tumors. This study establishes a foundation for three-dimensional quantitative analysis of microvasculature. It provides new tools and methods for diagnosis and monitoring of various diseases.
Abstract (translated)
URL
https://arxiv.org/abs/2604.04477
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NAIMA: Semantics Aware RGB Guided Depth Super-Resolution
2026-04-06 04:14:59Tayyab Nasir, Daochang Liu, Ajmal MianAbstract
Guided depth super-resolution (GDSR) is a multi-modal approach for depth map super-resolution that relies on a low-resolution depth map and a high-resolution RGB image to restore finer structural details. However, the misleading color and texture cues indicating depth discontinuities in RGB images often lead to artifacts and blurred depth boundaries in the generated depth map. We propose a solution that introduces global contextual semantic priors, generated from pretrained vision transformer token embeddings. Our approach to distilling semantic knowledge from pretrained token embeddings is motivated by their demonstrated effectiveness in related monocular depth estimation tasks. We introduce a Guided Token Attention (GTA) module, which iteratively aligns encoded RGB spatial features with depth encodings, using cross-attention for selectively injecting global semantic context extracted from different layers of a pretrained vision transformer. Additionally, we present an architecture called Neural Attention for Implicit Multi-token Alignment (NAIMA), which integrates DINOv2 with GTA blocks for a semantics-aware GDSR. Our proposed architecture, with its ability to distill semantic knowledge, achieves significant improvements over existing methods across multiple scaling factors and datasets.
Abstract (translated)
引导式深度超分辨率（GDSR）是一种基于多模态的深度图超分辨率方法，它依赖低分辨率深度图与高分辨率RGB图像来恢复更精细的结构细节。然而，RGB图像中误导性的颜色与纹理线索常导致深度不连续处的伪影与模糊边界。我们提出一种引入全局上下文语义先验的解决方案，该先验源自预训练视觉Transformer的标记嵌入。我们蒸馏预训练标记嵌入中语义知识的方法，受其在相关单目深度估计任务中已验证的有效性所启发。我们设计了引导式标记注意力（GTA）模块，通过交叉注意力机制迭代对齐RGB空间编码特征与深度编码，从而选择性注入从预训练视觉Transformer不同层提取的全局语义上下文。此外，我们提出了神经注意力隐式多标记对齐（NAIMA）架构，该架构将DINOv2与GTA模块集成，实现语义感知的GDSR。我们提出的架构凭借其语义知识蒸馏能力，在多种缩放因子与数据集上相较于现有方法取得了显著提升。
URL
https://arxiv.org/abs/2604.04407
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Physics-Informed Untrained Learning for RGB-Guided Superresolution Single-Pixel Hyperspectral Imaging
2026-04-04 03:48:08Hao Zhang, Bilige Xu, Lichen Wei, Xu Ma, Wenyi RenAbstract
Single-pixel imaging (SPI) offers a cost-effective route to hyperspectral acquisition but struggles to recover high-fidelity spatial and spectral details under extremely low sampling rates, a severely ill-posed inverse problem. While deep learning has shown potential, existing data-driven methods demand large-scale pretraining datasets that are often impractical in hyperspectral imaging. To overcome this limitation, we propose an end-to-end physics-informed framework that leverages untrained neural networks and RGB guidance for joint hyperspectral reconstruction and super-resolution without any external training data. The framework comprises three physically grounded stages: (1) a Regularized Least-Squares method with RGB-derived Grayscale Priors (LS-RGP) that initializes the solution by exploiting cross-modal structural correlations; (2) an Untrained Hyperspectral Recovery Network (UHRNet) that refines the reconstruction through measurement consistency and hybrid regularization; and (3) a Transformer-based Untrained Super-Resolution Network (USRNet) that upsamples the spatial resolution via cross-modal attention, transferring high-frequency details from the RGB guide. Extensive experiments on benchmark datasets demonstrate that our approach significantly surpasses state-of-the-art algorithms in both reconstruction accuracy and spectral fidelity. Moreover, a proof-of-concept experiment using a physical single-pixel imaging system validates the framework's practical applicability, successfully reconstructing a 144-band hyperspectral data cube at a mere 6.25% sampling rate. The proposed method thus provides a robust, data-efficient solution for computational hyperspectral imaging.
Abstract (translated)
单像素成像（SPI）为超光谱采集提供了一种经济高效的途径，但在极低采样率下难以恢复高保真空间和光谱细节，这是一个严重的病态逆问题。尽管深度学习已显示出潜力，但现有数据驱动方法需要大规模预训练数据集，这在超光谱成像中往往不切实际。为克服此限制，我们提出了一种端到端的物理信息框架，该框架利用未训练神经网络和RGB引导，无需任何外部训练数据即可实现联合超光谱重建与超分辨率。该框架包含三个基于物理的阶段：（1）基于RGB灰度先验的正则化最小二乘法（LS-RGP），通过利用跨模态结构相关性来初始化解；（2）未训练超光谱恢复网络（UHRNet），通过测量一致性和混合正则化来细化重建；（3）基于Transformer的未训练超分辨率网络（USRNet），通过跨模态注意力上采样空间分辨率，从RGB引导中转移高频细节。在基准数据集上的大量实验表明，我们的方法在重建精度和光谱保真度两方面均显著超越最先进算法。此外，使用物理单像素成像系统的概念验证实验验证了该框架的实际适用性，成功以仅6.25%的采样率重建了144波段超光谱数据立方体。因此，所提出的方法为计算超光谱成像提供了一种鲁棒、数据高效的解决方案。
URL
https://arxiv.org/abs/2604.03572
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DRIFT: Deep Restoration, ISP Fusion, and Tone-mapping
2026-04-03 19:04:36Soumendu Majee, Joshua Peter Ebenezer, Abhinau K. Venkataramanan, Weidi Liu, Thilo Balke, Zeeshan Nadir, Sreenithy Chandran, Seok-Jun Lee, Hamid Rahim SheikhAbstract
Smartphone cameras have gained immense popularity with the adoption of high-resolution and high-dynamic range imaging. As a result, high-performance camera Image Signal Processors (ISPs) are crucial in generating high-quality images for the end user while keeping computational costs low. In this paper, we propose DRIFT (Deep Restoration, ISP Fusion, and Tone-mapping): an efficient AI mobile camera pipeline that generates high quality RGB images from hand-held raw captures. The first stage of DRIFT is a Multi-Frame Processing (MFP) network that is trained using a adversarial perceptual loss to perform multi-frame alignment, denoising, demosaicing, and super-resolution. Then, the output of DRIFT-MFP is processed by a novel deep-learning based tone-mapping (DRIFT-TM) solution that allows for tone tunability, ensures tone-consistency with a reference pipeline, and can be run efficiently for high-resolution images on a mobile device. We show qualitative and quantitative comparisons against state-of-the-art MFP and tone-mapping methods to demonstrate the effectiveness of our approach.
Abstract (translated)
智能手机摄像头凭借高分辨率与高动态范围成像技术的普及而广受欢迎。因此，高性能的图像信号处理器（ISP）对于在保持低计算成本的同时向终端用户生成高质量图像至关重要。本文提出了一种名为DRIFT（深度复原、ISP融合与色调映射）的高效AI移动端摄像头成像管线，能够从手持RAW格式拍摄中生成高质量RGB图像。DRIFT的第一阶段是一个多帧处理（MFP）网络，该网络通过对抗性感知损失进行训练，以实现多帧对齐、去噪、去马赛克和超分辨率处理。随后，DRIFT-MFP的输出会经过一种新型的基于深度学习的色调映射（DRIFT-TM）方案处理，该方案具备色调可调性，能确保与参考管线保持色调一致性，并且可在移动设备上高效运行高分辨率图像。我们通过定性与定量对比当前最先进的多帧处理与色调映射方法，验证了本方法的有效性。
URL
https://arxiv.org/abs/2604.03402
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VOSR: A Vision-Only Generative Model for Image Super-Resolution
2026-04-03 17:50:29Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang, Xiangtao Kong, Jixin Zhao, Shihao Wang, Lei ZhangAbstract
Most of the recent generative image super-resolution (SR) methods rely on adapting large text-to-image (T2I) diffusion models pretrained on web-scale text-image data. While effective, this paradigm starts from a generic T2I generator, despite that SR is fundamentally a low-resolution (LR) input-conditioned image restoration task. In this work, we investigate whether an SR model trained purely on visual data can rival T2I-based ones. To this end, we propose VOSR, a Vision-Only generative framework for SR. We first extract semantically rich and spatially grounded features from the LR input using a pretrained vision encoder as visual semantic guidance. We then revisit classifier-free guidance for training generative models and show that the standard unconditional branch is ill-suited to restoration models trained from scratch. We therefore replace it with a restoration-oriented guidance strategy that preserves weak LR anchors. Built upon these designs, we first train a multi-step VOSR model from scratch and then distill it into a one-step model for efficient inference. VOSR requires less than one-tenth of the training cost of representative T2I-based SR methods, yet in both multi-step and one-step settings, it achieves competitive or even better perceptual quality and efficiency, while producing more faithful structures with fewer hallucinations on both synthetic and real-world benchmarks. Our results, for the first time, show that high-quality generative SR can be achieved without multimodal pretraining. The code and models can be found at this https URL.
Abstract (translated)
近期大多数生成式图像超分辨率（SR）方法依赖于在超大规模文本-图像数据上预训练的文本到图像（T2I）扩散模型。尽管有效，但该范式从通用T2I生成器出发，而超分辨率本质上是一个基于低分辨率（LR）输入条件的图像恢复任务。在本研究中，我们探索了仅通过视觉数据训练的SR模型是否能与基于T2I的方法相竞争。为此，我们提出了VOSR——一个面向超分辨率的纯视觉生成框架。我们首先使用预训练的视觉编码器从LR输入中提取语义丰富且空间定位的特征，作为视觉语义引导。随后，我们重新审视了生成模型训练中的无分类器引导方法，并证明标准的无条件分支不适合从零开始训练的恢复模型。因此，我们采用了一种面向恢复的引导策略来保留弱LR锚点。基于这些设计，我们首先从零开始训练多步VOSR模型，再将其蒸馏为单步模型以实现高效推理。VOSR的训练成本不到代表性T2I型SR方法的十分之一，但在多步和单步设置下均实现了具有竞争力甚至更优的感知质量和效率，同时在合成和真实世界基准测试中产生了更忠实结构且更少幻觉的结果。我们的研究首次表明，无需多模态预训练即可实现高质量生成式超分辨率。代码与模型可通过此https链接获取。
URL
https://arxiv.org/abs/2604.03225
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The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report
2026-04-03 17:17:26Bin Ren, Hang Guo, Yan Shu, Jiaqi Ma, Ziteng Cui, Shuhong Liu, Guofeng Mei, Lei Sun, Zongwei Wu, Fahad Shahbaz Khan, Salman Khan, Radu Timofte, Yawei Li, Hongyuan Yu, Pufan Xu, Chen Wu, Long Peng, Jiaojiao Yi, Siyang Yi, Yuning Cui, Jingyuan Xia, Xing Mou, Keji He, Jinlin Wu, Zongang Gao, Sen Yang, Rui Zheng, Fengguo Li, Yecheng Lei, Wenkai Min, Jie Liu, Keye Cao, Shubham Sharma, Manish Prasad, Haobo Li, Matin Fazel, Abdelhak Bentaleb, Rui Chen, Shurui Shi, Zitao Dai, Qingliang Liu, Yang Cheng, Jing Hu, Xuan Zhang, Rui Ding, Tingyi Zhang, Hui Deng, Mengyang Wang, Fulin Liu, Jing Wei, Qian Wang, Hongying Liu, Mingyang Li, Guanglu Dong, Zheng Yang, Chao Ren, Hongbo Fang, Lingxuan Li, Lin Si, Pan Gao, Moncef Gabbouj, Watchara Ruangsang, Supavadee AramvithAbstract
This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects, such as runtime, parameters, and FLOPs, while maintaining PSNR of around 26.90 dB on the DIV2K_LSDIR_valid dataset, and 26.99 dB on the DIV2K_LSDIR_test dataset. The challenge had 95 registered participants, and 15 teams made valid submissions. They gauge the state-of-the-art results for efficient single-image super-resolution.
Abstract (translated)
本文综述了NTIRE 2026高效单图像超分辨率挑战赛，重点关注参赛方案与结果。该挑战旨在设计一种网络，在维持DIV2K_LSDIR_valid数据集约26.90 dB、DIV2K_LSDIR_test数据集约26.99 dB的PSNR值的同时，降低运行时间、参数量或计算量等一个或多个指标。挑战赛共吸引95支注册队伍，其中15支提交了有效方案。这些方案评估了高效单图像超分辨率领域的最先进水平。
URL
https://arxiv.org/abs/2604.03198
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Super-Resolving Coarse-Resolution Weather Forecasts With Flow Matching
2026-04-01 13:43:42Aymeric Delefosse, Anastase Charantonis, Dominique B\'er\'eziatAbstract
Machine learning-based weather forecasting models now surpass state-of-the-art numerical weather prediction systems, but training and operating these models at high spatial resolution remains computationally expensive. We present a modular framework that decouples forecasting from spatial resolution by applying learned generative super-resolution as a post-processing step to coarse-resolution forecast trajectories. We formulate super-resolution as a stochastic inverse problem, using a residual formulation to preserve large-scale structure while reconstructing unresolved variability. The model is trained with flow matching exclusively on reanalysis data and is applied to global medium-range forecasts. We evaluate (i) design consistency by re-coarsening super-resolved forecasts and comparing them to the original coarse trajectories, and (ii) high-resolution forecast quality using standard ensemble verification metrics and spectral diagnostics. Results show that super-resolution preserves large-scale structure and variance after re-coarsening, introduces physically consistent small-scale variability, and achieves competitive probabilistic forecast skill at 0.25° resolution relative to an operational ensemble baseline, while requiring only a modest additional training cost compared with end-to-end high-resolution forecasting.
Abstract (translated)
基于机器学习的天气预报模型现已超越最先进的数值天气预报系统，但以高空间分辨率训练和运行这些模型仍具有较高的计算成本。我们提出了一种模块化框架，通过将学习到的生成式超分辨率作为粗分辨率预报轨迹的后处理步骤，将预报与空间分辨率解耦。我们将超分辨率公式化为随机逆问题，采用残差公式在重建未解析变化的同时保持大尺度结构。该模型仅使用再分析数据通过流匹配进行训练，并应用于全球中期预报。我们通过以下方式评估：(i) 通过将超分辨率预报重新粗化并与原始粗轨迹比较，检验设计一致性；(ii) 使用标准集合预报检验指标和谱诊断评估高分辨率预报质量。结果表明，超分辨率在重新粗化后保持了大尺度结构和方差，引入了物理一致的小尺度变化，并在0.25°分辨率下相对于业务集合预报基线实现了具有竞争力的概率预报技巧，而训练成本仅比端到端高分辨率预报略有增加。
URL
https://arxiv.org/abs/2604.00897
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TGIF2: Extended Text-Guided Inpainting Forgery Dataset & Benchmark
2026-03-30 15:59:16Hannes Mareen, Dimitrios Karageorgiou, Paschalis Giakoumoglou, Peter Lambert, Symeon Papadopoulos, Glenn Van WallendaelAbstract
Generative AI has made text-guided inpainting a powerful image editing tool, but at the same time a growing challenge for media forensics. Existing benchmarks, including our text-guided inpainting forgery (TGIF) dataset, show that image forgery localization (IFL) methods can localize manipulations in spliced images but struggle not in fully regenerated (FR) images, while synthetic image detection (SID) methods can detect fully regenerated images but cannot perform localization. With new generative inpainting models emerging and the open problem of localization in FR images remaining, updated datasets and benchmarks are needed. We introduce TGIF2, an extended version of TGIF, that captures recent advances in text-guided inpainting and enables a deeper analysis of forensic robustness. TGIF2 augments the original dataset with edits generated by FLUX.1 models, as well as with random non-semantic masks. Using the TGIF2 dataset, we conduct a forensic evaluation spanning IFL and SID, including fine-tuning IFL methods on FR images and generative super-resolution attacks. Our experiments show that both IFL and SID methods degrade on FLUX.1 manipulations, highlighting limited generalization. Additionally, while fine-tuning improves localization on FR images, evaluation with random non-semantic masks reveals object bias. Furthermore, generative super-resolution significantly weakens forensic traces, demonstrating that common image enhancement operations can undermine current forensic pipelines. In summary, TGIF2 provides an updated dataset and benchmark, which enables new insights into the challenges posed by modern inpainting and AI-based image enhancements. TGIF2 is available at this https URL.
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URL
https://arxiv.org/abs/2603.28613
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Dual-Path Learning based on Frequency Structural Decoupling and Regional-Aware Fusion for Low-Light Image Super-Resolution
2026-03-28 15:15:08Ji-Xuan He, Jia-Cheng Zhao, Feng-Qi Cui, Jinyang Huang, Yang Liu, Sirui Zhao, Meng Li, Zhi LiuAbstract
Low-light image super-resolution (LLISR) is essential for restoring fine visual details and perceptual quality under insufficient illumination conditions with ubiquitous low-resolution devices. Although pioneer methods achieve high performance on single tasks, they solve both tasks in a serial manner, which inevitably leads to artifact amplification, texture suppression, and structural degradation. To address this, we propose Decoupling then Perceive (DTP), a novel frequency-aware framework that explicitly separates luminance and texture into semantically independent components, enabling specialized modeling and coherent reconstruction. Specifically, to adaptively separate the input into low-frequency luminance and high-frequency texture subspaces, we propose a Frequency-aware Structural Decoupling (FSD) mechanism, which lays a solid foundation for targeted representation learning and reconstruction. Based on the decoupled representation, a Semantics-specific Dual-path Representation (SDR) learning strategy that performs targeted enhancement and reconstruction for each frequency component is further designed, facilitating robust luminance adjustment and fine-grained texture recovery. To promote structural consistency and perceptual alignment in the reconstructed output, building upon this dual-path modeling, we further introduce a Cross-frequency Semantic Recomposition (CSR) module that selectively integrates the decoupled representations. Extensive experiments on the most widely used LLISR benchmarks demonstrate the superiority of our DTP framework, improving $+$1.6\% PSNR, $+$9.6\% SSIM, and $-$48\% LPIPS compared to the most state-of-the-art (SOTA) algorithm. Codes are released at this https URL.
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URL
https://arxiv.org/abs/2603.27301
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Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays
2026-03-27 15:21:47Martin Rath, Morteza Ghahremani, Yitong Li, Ashkan Taghipour, Marcus Makowski, Christian WachingerAbstract
Computed tomography (CT) provides rich 3D anatomical details but is often constrained by high radiation exposure, substantial costs, and limited availability. While standard chest X-rays are cost-effective and widely accessible, they only provide 2D projections with limited pathological information. Reconstructing 3D CT volumes from 2D X-rays offers a transformative solution to increase diagnostic accessibility, yet existing methods predominantly rely on synthetic X-ray projections, limiting clinical generalization. In this work, we propose AXON, a multi-stage diffusion-based framework that reconstructs high-fidelity 3D CT volumes directly from real X-rays. AXON employs a coarse-to-fine strategy, with a Brownian Bridge diffusion model-based initial stage for global structural synthesis, followed by a ControlNet-based refinement stage for local intensity optimization. It also supports bi-planar X-ray input to mitigate depth ambiguities inherent in 2D-to-3D reconstruction. A super-resolution network is integrated to upscale the generated volumes to achieve diagnostic-grade resolution. Evaluations on both public and external datasets demonstrate that AXON significantly outperforms state-of-the-art baselines, achieving a 11.9% improvement in PSNR and a 11.0% increase in SSIM with robust generalizability across disparate clinical distributions. Our code is available at this https URL.
Abstract (translated)
计算机断层扫描（CT）可提供丰富的三维解剖细节，但常受限于高辐射暴露、高昂成本及可及性有限。而标准胸部X光片虽成本效益高且广泛可及，却仅能提供二维投影，病理信息有限。从二维X光重建三维CT体积为实现诊断可及性提升提供了变革性方案，但现有方法多依赖合成X光投影，限制了临床泛化能力。本研究提出AXON——一个基于多阶段扩散模型的框架，可直接从真实X光重建高保真三维CT体积。AXON采用由粗到细策略：首阶段基于布朗桥扩散模型进行全局结构合成，随后通过ControlNet进行局部强度优化。该框架同时支持双平面X光输入以缓解二维至三维重建固有的深度模糊问题，并集成超分辨率网络将生成体积上采样至诊断级分辨率。在公开及外部数据集上的评估表明，AXON显著优于现有最先进方法，在PSNR上提升11.9%，SSIM上提升11.0%，且在不同临床分布中展现出稳健的泛化能力。代码已公开于此https URL。
URL
https://arxiv.org/abs/2603.26509
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InstaVSR: Taming Diffusion for Efficient and Temporally Consistent Video Super-Resolution
2026-03-27 07:33:13Jintong Hu, Bin Chen, Zhenyu Hu, Jiayue Liu, Guo Wang, Lu QiAbstract
Video super-resolution (VSR) seeks to reconstruct high-resolution frames from low-resolution inputs. While diffusion-based methods have substantially improved perceptual quality, extending them to video remains challenging for two reasons: strong generative priors can introduce temporal instability, and multi-frame diffusion pipelines are often too expensive for practical deployment. To address both challenges simultaneously, we propose InstaVSR, a lightweight diffusion framework for efficient video super-resolution. InstaVSR combines three ingredients: (1) a pruned one-step diffusion backbone that removes several costly components from conventional diffusion-based VSR pipelines, (2) recurrent training with flow-guided temporal regularization to improve frame-to-frame stability, and (3) dual-space adversarial learning in latent and pixel spaces to preserve perceptual quality after backbone simplification. On an NVIDIA RTX 4090, InstaVSR processes a 30-frame video at 2K$\times$2K resolution in under one minute with only 7 GB of memory usage, substantially reducing the computational cost compared to existing diffusion-based methods while maintaining favorable perceptual quality with significantly smoother temporal transitions.
Abstract (translated)
视频超分辨率（VSR）旨在从低分辨率输入重建高分辨率帧。尽管基于扩散的方法已显著提升了感知质量，但将其扩展到视频领域仍面临两大挑战：强大的生成先验可能引入时间不稳定性，而多帧扩散流程通常因计算成本过高难以实际部署。为同时应对这两项挑战，我们提出了InstaVSR——一个轻量级的高效视频超分辨率扩散框架。InstaVSR融合三项核心技术：（1）通过剪枝单步扩散主干网络，移除了传统基于扩散的VSR流程中多个高成本组件；（2）采用基于光流引导的时间正则化循环训练，以提升帧间稳定性；（3）在隐空间与像素空间实施双空间对抗学习，确保主干简化后仍能保持感知质量。在NVIDIA RTX 4090显卡上，InstaVSR处理30帧2K×2K分辨率视频耗时不足一分钟，内存占用仅7GB，相较于现有基于扩散的方法大幅降低计算成本，同时保持了更优的感知质量与显著更平滑的时间过渡效果。
URL
https://arxiv.org/abs/2603.26134
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ScrollScape: Unlocking 32K Image Generation With Video Diffusion Priors
2026-03-25 13:03:02Haodong Yu, Yabo Zhang, Donglin Di, Ruyi Zhang, Wangmeng ZuoAbstract
While diffusion models excel at generating images with conventional dimensions, pushing them to synthesize ultra-high-resolution imagery at extreme aspect ratios (EAR) often triggers catastrophic structural failures, such as object repetition and spatial this http URL limitation fundamentally stems from a lack of robust spatial priors, as static text-to-image models are primarily trained on image distributions with conventional this http URL overcome this bottleneck, we present ScrollScape, a novel framework that reformulates EAR image synthesis into a continuous video generation process through two core this http URL mapping the spatial expansion of a massive canvas to the temporal evolution of video frames, ScrollScape leverages the inherent temporal consistency of video models as a powerful global constraint to ensure long-range structural this http URL, Scanning Positional Encoding (ScanPE) distributes global coordinates across frames to act as a flexible moving camera, while Scrolling Super-Resolution (ScrollSR) leverages video super-resolution priors to circumvent memory bottlenecks, efficiently scaling outputs to an unprecedented 32K resolution. Fine-tuned on a curated 3K multi-ratio image dataset, ScrollScape effectively aligns pre-trained video priors with the EAR generation task. Extensive evaluations demonstrate that it significantly outperforms existing image-diffusion baselines by eliminating severe localized artifacts. Consequently, our method overcomes inherent structural bottlenecks to ensure exceptional global coherence and visual fidelity across diverse domains at extreme scales.
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尽管扩散模型在生成常规尺寸图像方面表现出色，但将其推向极端纵横比（EAR）的超高分辨率图像合成时，常会引发灾难性的结构失效，例如物体重复和空间局限。这一根本限制源于缺乏稳健的空间先验，因为静态文本到图像模型主要基于常规尺寸图像分布进行训练。为突破这一瓶颈，我们提出了ScrollScape，一种新颖框架，通过两个核心机制将EAR图像合成重新构建为连续视频生成过程：通过将超大画布的空间扩展映射到视频帧的时间演变，ScrollScape利用视频模型固有的时间一致性作为强大的全局约束，以确保长程结构连贯性。扫描位置编码（ScanPE）将全局坐标分布到各帧中，充当灵活的移动摄像机；而滚动超分辨率（ScrollSR）则利用视频超分辨率先验来规避内存瓶颈，高效将输出扩展至前所未有的32K分辨率。经精心策划的3K多纵横比图像数据集微调后，ScrollScape能有效将预训练的视频先验与EAR生成任务对齐。广泛评估表明，该方法通过消除严重的局部伪影，显著优于现有图像扩散基线。因此，我们的方法克服了固有的结构瓶颈，确保了在极端尺度下跨不同领域的卓越全局连贯性和视觉保真度。
URL
https://arxiv.org/abs/2603.24270
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InstanceRSR: Real-World Super-Resolution via Instance-Aware Representation Alignment
2026-03-25 12:27:58Zixin Guo, Kai Zhao, Luyan ZhangAbstract
Existing real-world super-resolution (RSR) methods based on generative priors have achieved remarkable progress in producing high-quality and globally consistent reconstructions. However, they often struggle to recover fine-grained details of diverse object instances in complex real-world scenes. This limitation primarily arises because commonly adopted denoising losses (e.g., MSE) inherently favor global consistency while neglecting instance-level perception and restoration. To address this issue, we propose InstanceRSR, a novel RSR framework that jointly models semantic information and introduces instance-level feature alignment. Specifically, we employ low-resolution (LR) images as global consistency guidance while jointly modeling image data and semantic segmentation maps to enforce semantic relevance during sampling. Moreover, we design an instance representation learning module to align the diffusion latent space with the instance latent space, enabling instance-aware feature alignment, and further incorporate a scale alignment mechanism to enhance fine-grained perception and detail recovery. Benefiting from these designs, our approach not only generates photorealistic details but also preserves semantic consistency at the instance level. Extensive experiments on multiple real-world benchmarks demonstrate that InstanceRSR significantly outperforms existing methods in both quantitative metrics and visual quality, achieving new state-of-the-art (SOTA) performance.
Abstract (translated)
现有基于生成先验的真实世界超分辨率（RSR）方法在生成高质量、全局一致的图像重建方面取得了显著进展。然而，它们在复杂真实场景中恢复多样目标实例的细粒度细节方面往往存在不足。这一局限性主要源于常用的去噪损失函数（如MSE）本质上倾向于全局一致性，而忽略了实例级的感知与修复。针对该问题，我们提出了InstanceRSR，一种新型RSR框架，通过联合建模语义信息并引入实例级特征对齐来解决问题。具体而言，我们采用低分辨率（LR）图像作为全局一致性指导，同时联合建模图像数据与语义分割图，以在采样过程中强化语义相关性。此外，我们设计了实例表征学习模块，将扩散隐空间与实例隐空间进行对齐，实现实例感知的特征对齐，并进一步引入尺度对齐机制以增强细粒度感知与细节恢复能力。得益于上述设计，我们的方法不仅能生成照片般真实的细节，还能在实例层面保持语义一致性。在多个真实场景基准数据集上的大量实验表明，InstanceRSR在定量指标与视觉质量上均显著优于现有方法，达到了新的最先进（SOTA）性能。
URL
https://arxiv.org/abs/2603.24240
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RefReward-SR: LR-Conditioned Reward Modeling for Preference-Aligned Super-Resolution
2026-03-25 11:20:51Yushuai Song, Weize Quan, Weining Wang, Jiahui Sun, Jing Liu, Meng Li, Pengbin Yu, Zhentao Chen, Wei Shen, Lunxi Yuan, Dong-ming YanAbstract
Recent advances in generative super-resolution (SR) have greatly improved visual realism, yet existing evaluation and optimization frameworks remain misaligned with human perception. Full-Reference and No-Reference metrics often fail to reflect perceptual preference, either penalizing semantically plausible details due to pixel misalignment or favoring visually sharp but inconsistent artifacts. Moreover, most SR methods rely on ground-truth (GT)-dependent distribution matching, which does not necessarily correspond to human judgments. In this work, we propose RefReward-SR, a low-resolution (LR) reference-aware reward model for preference-aligned SR. Instead of relying on GT supervision or NR evaluation, RefReward-SR assesses high-resolution (HR) reconstructions conditioned on their LR inputs, treating the LR image as a semantic anchor. Leveraging the visual-linguistic priors of a Multimodal Large Language Models (MLLM), it evaluates semantic consistency and plausibility in a reasoning-aware manner. To support this paradigm, we construct RefSR-18K, the first large-scale LR-conditioned preference dataset for SR, providing pairwise rankings based on LR-HR consistency and HR naturalness. We fine-tune the MLLM with Group Relative Policy Optimization (GRPO) using LR-conditioned ranking rewards, and further integrate GRPO into SR model training with RefReward-SR as the core reward signal for preference-aligned generation. Extensive experiments show that our framework achieves substantially better alignment with human judgments, producing reconstructions that preserve semantic consistency while enhancing perceptual plausibility and visual naturalness. Code, models, and datasets will be released upon paper acceptance.
Abstract (translated)
生成式超分辨率（SR）的最新进展显著提升了视觉真实感，但现有的评估与优化框架仍与人类感知存在偏差。全参考与无参考指标常因像素错位而惩罚语义合理的细节，或偏好视觉锐利但不一致的伪影，难以反映感知偏好。此外，多数SR方法依赖真值（GT）监督的分布匹配，这与人类判断并不必然一致。本研究提出RefReward-SR——一种基于低分辨率（LR）参考的感知对齐奖励模型。该方法不依赖GT监督或无参考评估，而是以LR图像作为语义锚点，评估其条件生成的高分辨率（HR）重建结果。通过利用多模态大语言模型（MLLM）的视觉-语言先验，RefReward-SR以具备推理能力的方式评估语义一致性与合理性。为支持该范式，我们构建了首个大规模LR条件偏好数据集RefSR-18K，提供基于LR-HR一致性与HR自然度的成对排序。我们采用组相对策略优化（GRPO）对MLLM进行微调，使用LR条件排序奖励，并将GRPO进一步集成到SR模型训练中，以RefReward-SR作为偏好对齐生成的核心奖励信号。大量实验表明，本框架与人类判断的契合度显著提升，所生成的重建结果在增强感知合理性与视觉自然度的同时，保持了语义一致性。论文接受后，代码、模型与数据集将公开释放。
URL
https://arxiv.org/abs/2603.24198
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