Super_Resolution
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VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution
2026-03-24 12:54:05August Leander H{\o}eg, Sophia Wiinberg Bardenfleth, Hans Martin Kjer, Tim Bj{\o}rn Dyrby, Vedrana Andersen Dahl, Anders Bjorholm DahlAbstract
Recent advances in volumetric super-resolution (SR) have demonstrated strong performance in medical and scientific imaging, with transformer- and CNN-based approaches achieving impressive results even at extreme scaling factors. In this work, we show that much of this performance stems from training on downsampled data rather than real low-resolution scans. This reliance on downsampling is partly driven by the scarcity of paired high- and low-resolution 3D datasets. To address this, we introduce VoDaSuRe, a large-scale volumetric dataset containing paired high- and low-resolution scans. When training models on VoDaSuRe, we reveal a significant discrepancy: SR models trained on downsampled data produce substantially sharper predictions than those trained on real low-resolution scans, which smooth fine structures. Conversely, applying models trained on downsampled data to real scans preserves more structure but is inaccurate. Our findings suggest that current SR methods are overstated - when applied to real data, they do not recover structures lost in low-resolution scans and instead predict a smoothed average. We argue that progress in deep learning-based volumetric SR requires datasets with paired real scans of high complexity, such as VoDaSuRe. Our dataset and code are publicly available through: this https URL
Abstract (translated)
近期在体积超分辨率（SR）领域的研究进展已在医学与科学影像中展现出卓越性能，基于Transformer和CNN的方法甚至在极端缩放因子下仍能取得令人瞩目的成果。本研究指出，此类性能在很大程度上源于模型在降采样数据而非真实低分辨率扫描数据上的训练。这种对降采样的依赖部分源于成对高-低分辨率3D数据集的匮乏。为此，我们引入了VoDaSuRe——一个包含成对高-低分辨率扫描的大规模体积数据集。在该数据集上训练模型时，我们发现显著差异：在降采样数据上训练的SR模型能生成更锐利的预测结果，而在真实低分辨率扫描数据上训练的模型则会使精细结构平滑化。反之，将在降采样数据上训练的模型应用于真实扫描时，虽能保留更多结构但准确性不足。我们的研究表明，当前SR方法存在高估现象——当应用于真实数据时，它们无法恢复低分辨率扫描中丢失的结构，反而输出平滑化的平均预测。我们认为，基于深度学习的体积超分辨率研究需要像VoDaSuRe这样包含高复杂度真实成对扫描的数据集来推动进展。本数据集及代码已公开，可通过此链接获取。
URL
https://arxiv.org/abs/2603.23153
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From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery
2026-03-24 03:50:41Bijay Shakya, Catherine Hoier, Khandaker Mamun AhmedAbstract
Rapid and accurate structural damage assessment following natural disasters is critical for effective emergency response and recovery. However, remote sensing imagery often suffers from low spatial resolution, contextual ambiguity, and limited semantic interpretability, reducing the reliability of traditional detection pipelines. In this work, we propose a novel hybrid framework that integrates AI-based super-resolution, deep learning object detection, and Vision-Language Models (VLMs) for comprehensive post-disaster building damage assessment. First, we enhance pre- and post-disaster satellite imagery using a Video Restoration Transformer (VRT) to upscale images from 1024x1024 to 4096x4096 resolution, improving structural detail visibility. Next, a YOLOv11-based detector localizes buildings in pre-disaster imagery, and cropped building regions are analyzed using VLMs to semantically assess structural damage across four severity levels. To ensure robust evaluation in the absence of ground-truth captions, we employ CLIPScore for reference-free semantic alignment and introduce a multi-model VLM-as-a-Jury strategy to reduce individual model bias in safety-critical decision making. Experiments on subsets of the xBD dataset, including the Moore Tornado and Hurricane Matthew events, demonstrate that the proposed framework enhances the semantic interpretation of damaged buildings. In addition, our framework provides helpful recommendations to first responders for recovery based on damage analysis.
Abstract (translated)
自然灾害后快速准确的结构损伤评估对于有效的应急响应和恢复至关重要。然而，遥感影像常受限于空间分辨率低、上下文模糊以及语义可解释性不足，这降低了传统检测管线的可靠性。本研究提出一种新颖的混合框架，该框架整合了基于AI的超分辨率技术、深度学习目标检测以及视觉语言模型（VLMs），用于综合灾后建筑损伤评估。首先，我们采用视频恢复Transformer（VRT）对灾前灾后卫星影像进行增强，将图像从1024×1024分辨率提升至4096×4096，从而改善结构细节的可视性。随后，基于YOLOv11的检测器在灾前影像中定位建筑物，并对裁剪后的建筑区域使用VLMs进行语义分析，按四个严重程度等级评估结构损伤。为确保在缺乏真实标注描述的情况下进行稳健评估，我们采用CLIPScore进行无参考语义对齐，并引入多模型VLM-as-a-Jury策略以减少安全关键决策中的个体模型偏差。在xBD数据集子集（包括摩尔龙卷风和飓风马修事件）上的实验表明，所提框架增强了受损建筑的语义解释能力。此外，我们的框架还能基于损伤分析为救援人员提供恢复建议。
URL
https://arxiv.org/abs/2603.22768
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Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations
2026-03-23 23:00:18Heejong Kim, Abhishek Thanki, Roel van Herten, Daniel Margolis, Mert R SabuncuAbstract
Clinical MRI frequently acquires anisotropic volumes with high in-plane resolution and low through-plane resolution to reduce acquisition time. Multiple orientations are therefore acquired to provide complementary anatomical information. Conventional integration of these views relies on registration followed by interpolation, which can degrade fine structural details. Recent deep learning-based super-resolution (SR) approaches have demonstrated strong performance in enhancing single-view images. However, their clinical reliability is often limited by the need for large-scale training datasets, resulting in increased dependence on cohort-level priors. Self-supervised strategies offer an alternative by learning directly from the target scans. Prior work either neglects the existence of multi-view information or assumes that in-plane information can supervise through-plane reconstruction under the assumption of pre-alignment between images. However, this assumption is rarely satisfied in clinical settings. In this work, we introduce Single-Subject Implicit Multi-View Super-Resolution for MRI (SIMS-MRI), a framework that operates solely on anisotropic multi-view scans from a single patient without requiring pre- or post-processing. Our method combines a multi-resolution hash-encoded implicit representation with learned inter-view alignment to generate a spatially consistent isotropic reconstruction. We validate the SIMS-MRI pipeline on both simulated brain and clinical prostate MRI datasets. Code will be made publicly available for reproducibility: this https URL
Abstract (translated)
临床磁共振成像（MRI）常通过采用高平面分辨率、低层间分辨率的各向异性扫描来缩短采集时间，因此需采集多角度视图以提供互补的解剖信息。传统多视图融合方法依赖图像配准与插值，可能损害精细结构细节。近期基于深度学习的超分辨率（SR）方法在单视图图像增强上表现优异，但其临床可靠性常受限于大规模训练数据集的需求，导致对队列级先验的依赖增强。自监督策略提供了一种替代方案，可直接从目标扫描数据中学习。既往工作要么忽略多视图信息的存在，要么假设图像预对齐后平面信息可监督层间重建，然而该假设在临床场景中极少成立。本研究提出单被试隐式多视图MRI超分辨率（SIMS-MRI）框架，该框架仅基于单患者各向异性多视图扫描运行，无需预处理或后处理。我们的方法结合多分辨率哈希编码隐式表示与学习的视图间对齐，生成空间一致的各向同性重建。我们在模拟脑部与临床前列腺MRI数据集上验证了SIMS-MRI流程。代码将公开以确保可复现性：此 https URL
URL
https://arxiv.org/abs/2603.22627
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DUO-VSR: Dual-Stream Distillation for One-Step Video Super-Resolution
2026-03-23 17:56:17Zhengyao Lv, Menghan Xia, Xintao Wang, Kwan-Yee K. WongAbstract
Diffusion-based video super-resolution (VSR) has recently achieved remarkable fidelity but still suffers from prohibitive sampling costs. While distribution matching distillation (DMD) can accelerate diffusion models toward one-step generation, directly applying it to VSR often results in training instability alongside degraded and insufficient supervision. To address these issues, we propose DUO-VSR, a three-stage framework built upon a Dual-Stream Distillation strategy that unifies distribution matching and adversarial supervision for one-step VSR. Firstly, a Progressive Guided Distillation Initialization is employed to stabilize subsequent training through trajectory-preserving distillation. Next, the Dual-Stream Distillation jointly optimizes the DMD and Real-Fake Score Feature GAN (RFS-GAN) streams, with the latter providing complementary adversarial supervision leveraging discriminative features from both real and fake score models. Finally, a Preference-Guided Refinement stage further aligns the student with perceptual quality preferences. Extensive experiments demonstrate that DUO-VSR achieves superior visual quality and efficiency over previous one-step VSR approaches.
Abstract (translated)
基于扩散模型的视频超分辨率（VSR）技术近期在保真度上取得了显著进展，但仍受制于极高的采样成本。尽管分布匹配蒸馏（DMD）能够加速扩散模型实现一步生成，但直接将其应用于VSR常导致训练不稳定，并伴随监督信号退化与不足。针对这些问题，我们提出了DUO-VSR——一个基于双流蒸馏策略构建的三阶段框架，该策略统一了分布匹配与对抗式监督以实现一步VSR。首先，采用渐进式引导蒸馏初始化，通过轨迹保持蒸馏稳定后续训练。其次，双流蒸馏联合优化DMD流与真伪分数特征GAN（RFS-GAN）流，后者利用真实与伪造分数模型的判别特征提供互补的对抗监督。最后，通过偏好引导精炼阶段进一步使学生模型与感知质量偏好对齐。大量实验表明，DUO-VSR在视觉质量和效率上均优于此前的一步VSR方法。
URL
https://arxiv.org/abs/2603.22271
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Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model
2026-03-23 13:49:06SII-GAIR, Sand. ai, , Ethan Chern, Hansi Teng, Hanwen Sun, Hao Wang, Hong Pan, Hongyu Jia, Jiadi Su, Jin Li, Junjie Yu, Lijie Liu, Lingzhi Li, Lyumanshan Ye, Min Hu, Qiangang Wang, Quanwei Qi, Steffi Chern, Tao Bu, Taoran Wang, Teren Xu, Tianning Zhang, Tiantian Mi, Weixian Xu, Wenqiang Zhang, Wentai Zhang, Xianping Yi, Xiaojie Cai, Xiaoyang Kang, Yan Ma, Yixiu Liu, Yunbo Zhang, Yunpeng Huang, Yutong Lin, Zewei Tao, Zhaoliang Liu, Zheng Zhang, Zhiyao Cen, Zhixuan Yu, Zhongshu Wang, Zhulin Hu, Zijin Zhou, Zinan Guo, Yue Cao, Pengfei LiuAbstract
We present daVinci-MagiHuman, an open-source audio-video generative foundation model for human-centric generation. daVinci-MagiHuman jointly generates synchronized video and audio using a single-stream Transformer that processes text, video, and audio within a unified token sequence via self-attention only. This single-stream design avoids the complexity of multi-stream or cross-attention architectures while remaining easy to optimize with standard training and inference infrastructure. The model is particularly strong in human-centric scenarios, producing expressive facial performance, natural speech-expression coordination, realistic body motion, and precise audio-video synchronization. It supports multilingual spoken generation across Chinese (Mandarin and Cantonese), English, Japanese, Korean, German, and French. For efficient inference, we combine the single-stream backbone with model distillation, latent-space super-resolution, and a Turbo VAE decoder, enabling generation of a 5-second 256p video in 2 seconds on a single H100 GPU. In automatic evaluation, daVinci-MagiHuman achieves the highest visual quality and text alignment among leading open models, along with the lowest word error rate (14.60%) for speech intelligibility. In pairwise human evaluation, it achieves win rates of 80.0% against Ovi 1.1 and 60.9% against LTX 2.3 over 2000 comparisons. We open-source the complete model stack, including the base model, the distilled model, the super-resolution model, and the inference codebase.
Abstract (translated)
我们推出daVinci-MagiHuman，这是一个面向人类中心生成的开源音视频生成基础模型。daVinci-MagiHuman采用仅通过自注意力机制在统一标记序列中处理文本、视频和音频的单流Transformer架构，联合生成同步的视频与音频。这种单流设计避免了多流或交叉注意力架构的复杂性，同时易于使用标准训练和推理基础设施进行优化。该模型在人类中心场景中表现尤为突出，能够生成富有表现力的面部表演、自然的语音-表情协调、逼真的身体动作以及精确的音视频同步。它支持中文（普通话和粤语）、英语、日语、韩语、德语和法语的多语言口语生成。为实现高效推理，我们将单流主干网络与模型蒸馏、潜在空间超分辨率以及Turbo VAE解码器相结合，可在单张H100 GPU上2秒内生成5秒256p视频。在自动评估中，daVinci-MagiHuman在视觉质量和文本对齐方面领先于其他主流开源模型，并以14.60%的最低词错误率实现语音可懂度最优。在成对人工评估中，基于2000次对比，该模型对Ovi 1.1的胜率达80.0%，对LTX 2.3的胜率达60.9%。我们开源了完整的模型技术栈，包括基础模型、蒸馏模型、超分辨率模型及推理代码库。
URL
https://arxiv.org/abs/2603.21986
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OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging
2026-03-23 07:38:08Meilin Liu, Jiaying Wang, Jing ShanAbstract
Federated learning (FL) has become a promising paradigm for collaborative medical image analysis, yet existing frameworks remain tightly coupled to task-specific backbones and are fragile under heterogeneous imaging modalities. Such constraints hinder real-world deployment, where institutions vary widely in modality distributions and must support diverse downstream tasks. To address this limitation, we propose OmniFM, a modality- and task-agnostic FL framework that unifies training across classification, segmentation, super-resolution, visual question answering, and multimodal fusion without re-engineering the optimization pipeline. OmniFM builds on a key frequency-domain insight: low-frequency spectral components exhibit strong cross-modality consistency and encode modality-invariant anatomical structures. Accordingly, OmniFM integrates (i) Global Spectral Knowledge Retrieval to inject global frequency priors, (ii) Embedding-wise Cross-Attention Fusion to align representations, and (iii) Prefix-Suffix Spectral Prompting to jointly condition global and personalized cues, together regularized by a Spectral-Proximal Alignment objective that stabilizes aggregation. Experiments on real-world datasets show that OmniFM consistently surpasses state-of-the-art FL baselines across intra- and cross-modality heterogeneity, achieving superior results under both fine-tuning and training-from-scratch setups.
Abstract (translated)
联邦学习（FL）已成为协作式医学图像分析的一种有前景范式，但现有框架仍与特定任务架构紧密耦合，且在异构成像模态条件下表现脆弱。此类限制阻碍了真实场景部署——不同机构的模态分布差异显著，且需支持多样化的下游任务。为突破此局限，我们提出OmniFM，一个模态与任务无关的联邦学习框架，可在无需重构优化流程的前提下，统一实现分类、分割、超分辨率、视觉问答及多模态融合等任务的训练。OmniFM基于一个关键频域洞察：低频谱分量呈现强跨模态一致性，并编码模态不变解剖结构。据此，OmniFM整合了（i）全局频谱知识检索以注入全局频率先验，（ii）逐嵌入跨注意力融合以对齐表征，以及（iii）前缀-后缀频谱提示以联合约束全局与个性化线索，并通过频谱近端对齐目标进行正则化以稳定聚合。在真实数据集上的实验表明，OmniFM在模态内与跨模态异构场景下持续超越最先进联邦学习基线，在微调与从头训练设置中均取得更优结果。
URL
https://arxiv.org/abs/2603.21660
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Unregistered Spectral Image Fusion: Unmixing, Adversarial Learning, and Recoverability
2026-03-23 02:55:16Jiahui Song, Sagar Shrestha, Xiao FuAbstract
This paper addresses the fusion of a pair of spatially unregistered hyperspectral image (HSI) and multispectral image (MSI) covering roughly overlapping regions. HSIs offer high spectral but low spatial resolution, while MSIs provide the opposite. The goal is to integrate their complementary information to enhance both HSI spatial resolution and MSI spectral resolution. While hyperspectral-multispectral fusion (HMF) has been widely studied, the unregistered setting remains challenging. Many existing methods focus solely on MSI super-resolution, leaving HSI unchanged. Supervised deep learning approaches were proposed for HSI super-resolution, but rely on accurate training data, which is often unavailable. Moreover, theoretical analyses largely address the co-registered case, leaving unregistered HMF poorly understood. In this work, an unsupervised framework is proposed to simultaneously super-resolve both MSI and HSI. The method integrates coupled spectral unmixing for MSI super-resolution with latent-space adversarial learning for HSI super-resolution. Theoretical guarantees on the recoverability of the super-resolution MSI and HSI are established under reasonable generative models -- providing, to our best knowledge, the first such insights for unregistered HMF. The approach is validated on semi-real and real HSI-MSI pairs across diverse conditions.
Abstract (translated)
本文针对空间未配准的高光谱图像（HSI）与多光谱图像（MSI）融合问题展开研究，两者覆盖区域大致重叠。高光谱图像具有高光谱分辨率但空间分辨率低，而多光谱图像则相反。研究目标在于整合二者互补信息，以同时提升高光谱图像的空间分辨率与多光谱图像的光谱分辨率。尽管高光谱-多光谱融合（HMF）已得到广泛研究，但未配准场景仍具挑战性。现有方法多仅关注多光谱图像超分辨率，未改变高光谱图像质量。虽有监督深度学习方法被提出用于高光谱超分辨率，但其依赖难以获取的精确训练数据。此外，现有理论分析多针对配准场景，未配准条件下的融合问题尚缺乏深入理解。 本研究提出一种无监督框架，可同时对多光谱与高光谱图像进行超分辨率重建。该方法将耦合光谱解混用于多光谱超分辨率，并结合潜在空间对抗学习实现高光谱超分辨率。在合理的生成模型下，本文建立了超分辨率重建多光谱与高光谱图像可恢复性的理论保证——据我们所知，这是首个针对未配准高光谱-多光谱融合的理论分析。通过在多样条件下对半真实与真实HSI-MSI数据对进行验证，证实了该方法的有效性。
URL
https://arxiv.org/abs/2603.21510
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LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction
2026-03-22 03:52:38Shuwei Huang, Shizhuo Liu, Zijun WeiAbstract
Diffusion-based image super-resolution (SR), which aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) observations, faces a fundamental trade-off between inference efficiency and reconstruction quality. The state-of-the-art residual-shifting diffusion framework achieves efficient 4-step inference, yet suffers from severe performance degradation in compact sampling trajectories. This is mainly attributed to two core limitations: the inherent suboptimality of unconstrained random Gaussian noise in intermediate steps, which leads to error accumulation and insufficient LR prior guidance, and the initialization bias caused by naive bicubic upsampling. In this paper, we propose LPNSR, a prior-enhanced efficient diffusion framework to address these issues. We first mathematically derive the closed-form analytical solution of the optimal intermediate noise for the residual-shifting diffusion paradigm, and accordingly design an LR-guided multi-input-aware noise predictor to replace random Gaussian noise, embedding LR structural priors into the reverse process while fully preserving the framework's core efficient residual-shifting mechanism. We further mitigate initial bias with a high-quality pre-upsampling network to optimize the diffusion starting point. With a compact 4-step trajectory, LPNSR can be optimized in an end-to-end manner. Extensive experiments demonstrate that LPNSR achieves state-of-the-art perceptual performance on both synthetic and real-world datasets, without relying on any large-scale text-to-image priors. The source code of our method can be found at this https URL.
Abstract (translated)
基于扩散模型的图像超分辨率（SR）旨在从相应的低分辨率（LR）观测图像中重建高分辨率（HR）图像，其面临推理效率与重建质量之间的根本性权衡。最先进的残差移位扩散框架实现了高效的4步推理，但在紧凑采样轨迹中性能严重下降。这主要归因于两个核心局限：中间步骤中无约束随机高斯噪声的内在次优性导致误差累积和LR先验指导不足，以及朴素双三次上采样引起的初始化偏差。本文提出LPNSR，一种先验增强的高效扩散框架以解决这些问题。我们首先为残差移位扩散范式数学推导出最优中间噪声的闭式解析解，并据此设计LR引导的多输入感知噪声预测器以替代随机高斯噪声，将LR结构先验嵌入逆向过程，同时充分保留框架核心的高效残差移位机制。我们进一步通过高质量预上采样网络缓解初始偏差以优化扩散起点。在紧凑的4步轨迹下，LPNSR可实现端到端优化。大量实验表明，LPNSR在合成和真实世界数据集上均达到最先进的感知性能，且不依赖任何大规模文本到图像先验。我们方法的源代码可在此https URL找到。
URL
https://arxiv.org/abs/2603.21045
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MFSR: MeanFlow Distillation for One Step Real-World Image Super Resolution
2026-03-21 07:18:01Ruiqing Wang, Kai Zhang, Yuanzhi Zhu, Hanshu Yan, Shilin Lu, Jian YangAbstract
Diffusion- and flow-based models have advanced Real-world Image Super-Resolution (Real-ISR), but their multi-step sampling makes inference slow and hard to deploy. One-step distillation alleviates the cost, yet often degrades restoration quality and removes the option to refine with more steps. We present Mean Flows for Super-Resolution (MFSR), a new distillation framework that produces photorealistic results in a single step while still allowing an optional few-step path for further improvement. Our approach uses MeanFlow as the learning target, enabling the student to approximate the average velocity between arbitrary states of the Probability Flow ODE (PF-ODE) and effectively capture the teacher's dynamics without explicit rollouts. To better leverage pretrained generative priors, we additionally improve original MeanFlow's Classifier-Free Guidance (CFG) formulation with teacher CFG distillation strategy, which enhances restoration capability and preserves fine details. Experiments on both synthetic and real-world benchmarks demonstrate that MFSR achieves efficient, flexible, and high-quality super-resolution, delivering results on par with or even better than multi-step teachers while requiring much lower computational cost.
Abstract (translated)
扩散模型与流模型推动了真实世界图像超分辨率（Real-ISR）的发展，但其多步采样过程导致推理速度缓慢且难以部署。一步蒸馏虽能降低计算成本，却常损害修复质量并丧失多步精细调整的可能性。本文提出超分辨率平均流（MFSR）这一新型蒸馏框架，既能单步生成照片级真实结果，又保留可选的多步优化路径以实现进一步提升。该方法以平均流作为学习目标，使学生模型能够逼近概率流常微分方程（PF-ODE）任意状态间的平均速度，无需显式展开即可有效捕捉教师模型动态。为更好利用预训练生成先验，我们进一步改进原始平均流的无分类器引导（CFG）公式，通过教师CFG蒸馏策略增强修复能力并保留精细细节。在合成与真实世界基准测试中，MFSR均展现出高效、灵活且高质量的超分辨率性能，其效果与多步教师模型相当甚至更优，而计算成本显著降低。
URL
https://arxiv.org/abs/2603.20690
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Interpretable Operator Learning for Inverse Problems via Adaptive Spectral Filtering: Convergence and Discretization Invariance
2026-03-21 02:19:19Hang-Cheng Dong, Pengcheng Cheng, Shuhuan LiAbstract
Solving ill-posed inverse problems necessitates effective regularization strategies to stabilize the inversion process against measurement noise. While classical methods like Tikhonov regularization require heuristic parameter tuning, and standard deep learning approaches often lack interpretability and generalization across resolutions, we propose SC-Net (Spectral Correction Network), a novel operator learning framework. SC-Net operates in the spectral domain of the forward operator, learning a pointwise adaptive filter function that reweights spectral coefficients based on the signal-to-noise ratio. We provide a theoretical analysis showing that SC-Net approximates the continuous inverse operator, guaranteeing discretization invariance. Numerical experiments on 1D integral equations demonstrate that SC-Net: (1) achieves the theoretical minimax optimal convergence rate ($O(\delta^{0.5})$ for $s=p=1.5$), matching theoretical lower bounds; (2) learns interpretable sharp-cutoff filters that outperform Oracle Tikhonov regularization; and (3) exhibits zero-shot super-resolution, maintaining stable reconstruction errors ($\approx 0.23$) when trained on coarse grids ($N=256$) and tested on significantly finer grids (up to $N=2048$). The proposed method bridges the gap between rigorous regularization theory and data-driven operator learning.
Abstract (translated)
求解病态反问题需要有效的正则化策略，以稳定反演过程对测量噪声的鲁棒性。尽管经典方法（如吉洪诺夫正则化）依赖启发式参数调优，而标准深度学习方法常缺乏可解释性及跨分辨率泛化能力，我们提出了SC-Net（谱校正网络），一种新颖的算子学习框架。SC-Net在前向算子的谱域中运行，学习逐点自适应滤波函数，依据信噪比重新加权谱系数。我们提供理论分析表明SC-Net逼近连续反演算子，保证离散化不变性。在一维积分方程上的数值实验证明SC-Net：（1）达到理论极小极大最优收敛速率（当s=p=1.5时为O(δ^0.5)），匹配理论下界；（2）学习可解释的锐截止滤波器，性能超越最优吉洪诺夫正则化；（3）展现零样本超分辨率能力，在粗网格（N=256）上训练后，于显著更细网格（最高N=2048）测试时仍保持稳定重建误差（约0.23）。该方法弥合了严格正则化理论与数据驱动算子学习之间的差距。
URL
https://arxiv.org/abs/2603.20602
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Generalizable NGP-SR: Generalizable Neural Radiance Fields Super-Resolution via Neural Graph Primitives
2026-03-20 16:50:16Wanqi Yuan, Omkar Sharad Mayekar, Connor Pennington, Nianyi LiAbstract
Neural Radiance Fields (NeRF) achieve photorealistic novel view synthesis but become costly when high-resolution (HR) rendering is required, as HR outputs demand dense sampling and higher-capacity models. Moreover, naively super-resolving per-view renderings in 2D often breaks multi-view consistency. We propose Generalizable NGP-SR, a 3D-aware super-resolution framework that reconstructs an HR radiance field directly from low-resolution (LR) posed images. Built on Neural Graphics Primitives (NGP), NGP-SR conditions radiance prediction on 3D coordinates and learned local texture tokens, enabling recovery of high-frequency details within the radiance field and producing view-consistent HR novel views without external HR references or post-hoc 2D upsampling. Importantly, our model is generalizable: once trained, it can be applied to unseen scenes and rendered from novel viewpoints without per-scene optimization. Experiments on multiple datasets show that NGP-SR consistently improves both reconstruction quality and runtime efficiency over prior NeRF-based super-resolution methods, offering a practical solution for scalable high-resolution novel view synthesis.
Abstract (translated)
神经辐射场（NeRF）可实现照片级真实感的新视角合成，但当需要高分辨率（HR）渲染时，其计算成本高昂，因为高分辨率输出需要密集采样和更高容量的模型。此外，简单地以2D方式对单视角渲染结果进行超分辨率处理，往往会破坏多视角一致性。我们提出了通用型NGP-SR，这是一个三维感知的超分辨率框架，可直接从低分辨率（LR）的位姿图像中重建高分辨率辐射场。基于神经图形基元（NGP），NGP-SR将辐射预测条件化于三维坐标与学习得到的局部纹理标记，从而在辐射场内恢复高频细节，并生成视角一致的高分辨率新视图，而无需外部高分辨率参考或事后的2D上采样。重要的是，我们的模型具有通用性：训练完成后，可应用于未见场景并从新视角进行渲染，无需针对每个场景进行优化。在多个数据集上的实验表明，NGP-SR在重建质量和运行效率上均持续优于先前的基于NeRF的超分辨率方法，为可扩展的高分辨率新视角合成提供了实用解决方案。
URL
https://arxiv.org/abs/2603.20128
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CAFlow: Adaptive-Depth Single-Step Flow Matching for Efficient Histopathology Super-Resolution
2026-03-19 05:45:58Elad Yoshai, Ariel D. Yoshai, Natan T. ShakedAbstract
In digital pathology, whole-slide images routinely exceed gigapixel resolution, making computationally intensive generative super-resolution (SR) impractical for routine deployment. We introduce CAFlow, an adaptive-depth single-step flow-matching framework that routes each image tile to the shallowest network exit that preserves reconstruction quality. CAFlow performs flow matching in pixel-unshuffled rearranged space, reducing spatial computation by 16x while enabling direct inference. We show that dedicating half of training to exact t=0 samples is essential for single-step quality (-1.5 dB without it). The backbone, FlowResNet (1.90M parameters), mixes convolution and window self-attention blocks across four early exits spanning 3.1 to 13.3 GFLOPs. A lightweight exit classifier (~6K parameters) achieves 33% compute savings at only 0.12 dB cost. On multi-organ histopathology x4 SR, adaptive routing achieves 31.72 dB PSNR versus 31.84 dB at full depth, while the shallowest exit exceeds bicubic by +1.9 dB at 2.8x less compute than SwinIR-light. The method generalizes to held-out colon tissue with minimal quality loss (-0.02 dB), and at x8 upscaling it outperforms all comparable-compute baselines while remaining competitive with the much larger SwinIR-Medium model. Downstream nuclei segmentation confirms preservation of clinically relevant structure. The model trains in under 5 hours on a single GPU, and adaptive routing can reduce whole-slide inference from minutes to seconds.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18513
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Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI
2026-03-18 21:48:48Md Hasibul Husain Hisham, Shireen Elhabian, Ganesh Adluru, Jason Mendes, Andrew Arai, Eugene Kholmovski, Ravi Ranjan, Edward DiBellaAbstract
Accelerated 3D late gadolinium enhancement (LGE) MRI requires robust reconstruction methods to recover thin atrial structures from undersampled k-space data. While unrolled model-based networks effectively integrate physics-driven data consistency with learned priors, they operate at the acquired resolution and may fail to fully recover high-frequency detail. We propose a hybrid unrolled reconstruction framework in which an Enhanced Deep Super-Resolution (EDSR) network replaces the proximal operator within each iteration of the optimization loop, enabling joint super-resolution enhancement and data consistency enforcement. The model is trained end-to-end on retrospectively undersampled preclinical 3D LGE datasets and compared against compressed sensing, Model-Based Deep Learning (MoDL), and self-guided Deep Image Prior (DIP) baselines. Across acceleration factors, the proposed method consistently improves PSNR and SSIM over standard unrolled reconstruction and better preserves fine cardiac structures, leading to improved LA (left atrium) segmentation performance. These results demonstrate that integrating super-resolution priors directly within model-based reconstruction provides measurable gains in accelerated 3D LGE MRI.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18309
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ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation
2026-03-18 15:04:57Dmitriy Rivkin, Parker Ewen, Lili Gao, Julian Ost, Stefanie Walz, Rasika Kangutkar, Mario Bijelic, Felix HeideAbstract
Recent video diffusion models achieve high-quality generation through recurrent frame processing where each frame generation depends on previous frames. However, this recurrent mechanism means that training such models in the pixel domain incurs prohibitive memory costs, as activations accumulate across the entire video sequence. This fundamental limitation also makes fine-tuning these models with pixel-wise losses computationally intractable for long or high-resolution videos. This paper introduces ChopGrad, a truncated backpropagation scheme for video decoding, limiting gradient computation to local frame windows while maintaining global consistency. We provide a theoretical analysis of this approximation and show that it enables efficient fine-tuning with frame-wise losses. ChopGrad reduces training memory from scaling linearly with the number of video frames (full backpropagation) to constant memory, and compares favorably to existing state-of-the-art video diffusion models across a suite of conditional video generation tasks with pixel-wise losses, including video super-resolution, video inpainting, video enhancement of neural-rendered scenes, and controlled driving video generation.
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URL
https://arxiv.org/abs/2603.17812
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Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression
2026-03-18 06:35:53Xinning Chai, Zhengxue Cheng, Xin Li, Rong Xie, Li SongAbstract
Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates. However, most approaches require training separate diffusion models for each target bitrate, resulting in substantial computational overhead and hindering practical deployment. Meanwhile, recent studies have shown that joint super-resolution can serve as an effective approach for enhancing low-bitrate reconstruction. However, when moving toward ultra-low bitrate regimes, these methods struggle due to severe information loss, and their reliance on fixed super-resolution scales prevents flexible adaptation across diverse bitrates. To address these limitations, we propose ASSR-EIC, a novel image compression framework that leverages arbitrary-scale super-resolution (ASSR) to support variable-rate extreme image compression (EIC). An arbitrary-scale downsampling module is introduced at the encoder side to provide controllable rate reduction, while a diffusion-based, joint degradation-aware ASSR decoder enables rate-adaptive reconstruction within a single model. We exploit the compression- and rescaling-aware diffusion prior to guide the reconstruction, yielding high fidelity and high realism restoration across diverse compression and rescaling settings. Specifically, we design a global compression-rescaling adaptor that offers holistic guidance for rate adaptation, and a local compression-rescaling modulator that dynamically balances generative and fidelity-oriented behaviors to achieve fine-grained, bitrate-adaptive detail restoration. To further enhance reconstruction quality, we introduce a dual semantic-enhanced design. Extensive experiments demonstrate that ASSR-EIC delivers state-of-the-art performance in extreme image compression while simultaneously supporting flexible bitrate control and adaptive rate-dependent reconstruction.
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URL
https://arxiv.org/abs/2603.17408
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SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation
2026-03-17 17:59:30Jiongze Yu, Xiangbo Gao, Pooja Verlani, Akshay Gadde, Yilin Wang, Balu Adsumilli, Zhengzhong TuAbstract
Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference time: users cannot reliably correct unexpected artifacts, but instead can only accept whatever the model produces. In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control signal. Specifically, users can first super-resolve or optionally a small set of keyframes using any off-the-shelf image super-resolution (ISR) model, then SparkVSR propagates the keyframe priors to the entire video sequence while remaining grounded by the original LR video motion. Concretely, we introduce a keyframe-conditioned latent-pixel two-stage training pipeline that fuses LR video latents with sparsely encoded HR keyframe latents to learn robust cross-space propagation and refine perceptual details. At inference time, SparkVSR supports flexible keyframe selection (manual specification, codec I-frame extraction, or random sampling) and a reference-free guidance mechanism that continuously balances keyframe adherence and blind restoration, ensuring robust performance even when reference keyframes are absent or imperfect. Experiments on multiple VSR benchmarks demonstrate improved temporal consistency and strong restoration quality, surpassing baselines by up to 24.6%, 21.8%, and 5.6% on CLIP-IQA, DOVER, and MUSIQ, respectively, enabling controllable, keyframe-driven video super-resolution. Moreover, we demonstrate that SparkVSR is a generic interactive, keyframe-conditioned video processing framework as it can be applied out of the box to unseen tasks such as old-film restoration and video style transfer. Our project page is available at: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2603.16864
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RASLF: Representation-Aware State Space Model for Light Field Super-Resolution
2026-03-17 08:28:00Zeqiang Wei, Kai Jin, Kuan Song, Xiuzhuang Zhou, Wenlong Chen, Min XuAbstract
Current SSM-based light field super-resolution (LFSR) methods often fail to fully leverage the complementarity among various LF representations, leading to the loss of fine textures and geometric misalignments across views. To address these issues, we propose RASLF, a representation-aware state-space framework that explicitly models structural correlations across multiple LF representations. Specifically, a Progressive Geometric Refinement (PGR) block is created that uses a panoramic epipolar representation to explicitly encode multi-view parallax differences, thereby enabling integration across different LF representations. Furthermore, we introduce a Representation Aware Asymmetric Scanning (RAAS) mechanism that dynamically adjusts scanning paths based on the physical properties of different representation spaces, optimizing the balance between performance and efficiency through path pruning. Additionally, a Dual-Anchor Aggregation (DAA) module improves hierarchical feature flow, reducing redundant deeplayer features and prioritizing important reconstruction information. Experiments on various public benchmarks show that RASLF achieves the highest reconstruction accuracy while remaining highly computationally efficient.
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URL
https://arxiv.org/abs/2603.16243
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GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution
2026-03-16 15:24:39Qiaosi Yi, Shuai Li, Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang, Lei ZhangAbstract
Recently, reinforcement learning (RL) has been employed for improving generative image super-resolution (ISR) performance. However, the current efforts are focused on multi-step generative ISR, while one-step generative ISR remains underexplored due to its limited stochasticity. In addition, RL methods such as Direct Preference Optimization (DPO) require the generation of positive and negative sample pairs offline, leading to a limited number of samples, while Group Relative Policy Optimization (GRPO) only calculates the likelihood of the entire image, ignoring local details that are crucial for ISR. In this paper, we propose Group Direct Preference Optimization (GDPO), a novel approach to integrate RL into one-step generative ISR model training. First, we introduce a noise-aware one-step diffusion model that can generate diverse ISR outputs. To prevent performance degradation caused by noise injection, we introduce an unequal-timestep strategy to decouple the timestep of noise addition from that of diffusion. We then present the GDPO strategy, which integrates the principle of GRPO into DPO, to calculate the group-relative advantage of each online generated sample for model optimization. Meanwhile, an attribute-aware reward function is designed to dynamically evaluate the score of each sample based on its statistics of smooth and texture areas. Experiments demonstrate the effectiveness of GDPO in enhancing the performance of one-step generative ISR models. Code: this https URL.
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URL
https://arxiv.org/abs/2603.16769
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TextOVSR: Text-Guided Real-World Opera Video Super-Resolution
2026-03-16 11:48:43Hua Chang, Xin Xu, Wei Liu, Jiayi Wu, Kui Jiang, Fei Ma, Qi TianAbstract
Many classic opera videos exhibit poor visual quality due to the limitations of early filming equipment and long-term degradation during storage. Although real-world video super-resolution (RWVSR) has achieved significant advances in recent years, directly applying existing methods to degraded opera videos remains challenging. The difficulties are twofold. First, accurately modeling real-world degradations is complex: simplistic combinations of classical degradation kernels fail to capture the authentic noise distribution, while methods that extract real noise patches from external datasets are prone to style mismatches that introduce visual artifacts. Second, current RWVSR methods, which rely solely on degraded image features, struggle to reconstruct realistic and detailed textures due to a lack of high-level semantic guidance. To address these issues, we propose a Text-guided Dual-Branch Opera Video Super-Resolution (TextOVSR) network, which introduces two types of textual prompts to guide the super-resolution process. Specifically, degradation-descriptive text, derived from the degradation process, is incorporated into the negative branch to constrain the solution space. Simultaneously, content-descriptive text is incorporated into a positive branch and our proposed Text-Enhanced Discriminator (TED) to provide semantic guidance for enhanced texture reconstruction. Furthermore, we design a Degradation-Robust Feature Fusion (DRF) module to facilitate cross-modal feature fusion while suppressing degradation interference. Experiments on our OperaLQ benchmark show that TextOVSR outperforms state-of-the-art methods both qualitatively and quantitatively. The code is available at this https URL.
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URL
https://arxiv.org/abs/2603.15153
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Comparative Analysis of 3D Convolutional and 2.5D Slice-Conditioned U-Net Architectures for MRI Super-Resolution via Elucidated Diffusion Models
2026-03-15 23:48:29Hendrik Chiche, Ludovic Corcos, Logan RougeAbstract
Magnetic resonance imaging (MRI) super-resolution (SR) methods that computationally enhance low-resolution acquisitions to approximate high-resolution quality offer a compelling alternative to expensive high-field scanners. In this work we investigate an elucidated diffusion model (EDM) framework for brain MRI SR and compare two U-Net backbone architectures: (i) a full 3D convolutional U-Net that processes volumetric patches with 3D convolutions and multi-head self-attention, and (ii) a 2.5D slice-conditioned U-Net that super-resolves each slice independently while conditioning on an adjacent slice for inter-slice context. Both models employ continuous-sigma noise conditioning following Karras et al. and are trained on the NKI cohort of the FOMO60K dataset. On a held-out test set of 5 subjects (6 volumes, 993 slices), the 3D model achieves 37.75 dB PSNR, 0.997 SSIM, and 0.020 LPIPS, improving on the off-the-shelf pretrained EDSR baseline (35.57 dB / 0.024 LPIPS) and the 2.5D variant (35.82 dB) across all three metrics under the same test data and degradation pipeline.
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URL
https://arxiv.org/abs/2603.14667
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