Super_Resolution
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Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation
2025-12-03 02:48:25Zhenzhong Wang, Xin Zhang, Jun Liao, Min JiangAbstract
Multiphase flow simulation is critical in science and engineering but incurs high computational costs due to complex field discontinuities and the need for high-resolution numerical meshes. While Neural Operators (NOs) offer an efficient alternative for solving Partial Differential Equations (PDEs), they struggle with two core challenges unique to multiphase systems: spectral bias caused by spatial heterogeneity at phase interfaces, and the persistent scarcity of expensive, high-resolution field data. This work introduces the Interface Information Aware Neural Operator (IANO), a novel architecture that mitigates these issues by leveraging readily obtainable interface data (e.g., topology and position). Interface data inherently contains the high-frequency features not only necessary to complement the physical field data, but also help with spectral bias. IANO incorporates an interface-aware function encoding mechanism to capture dynamic coupling, and a geometry-aware positional encoding method to enhance spatial fidelity for pointwise super-resolution. Empirical results across multiple multiphase flow cases demonstrate that IANO achieves significant accuracy improvements (up to $\sim$10\%) over existing NO baselines. Furthermore, IANO exhibits superior generalization capabilities in low-data and noisy settings, confirming its utility for practical, data-efficient $\text{AI}$-based multiphase flow simulations.
Abstract (translated)
多相流模拟在科学和工程领域中至关重要，但由于相界面的复杂场不连续性和对高分辨率数值网格的需求，其计算成本高昂。虽然神经算子（NOs）为求解偏微分方程提供了一种高效的替代方案，但它们面临两个独特于多相系统的核心挑战：由相界面空间异质性引起的频谱偏差问题和昂贵的高质量场数据持续稀缺的问题。本文引入了接口信息感知神经算子（IANO），这是一种新型架构，通过利用易于获取的接口数据（如拓扑结构和位置）来缓解这些问题。接口数据本质上包含了物理场数据中缺失的高频特征，并有助于解决频谱偏差问题。IANO集成了一个接口感知的功能编码机制以捕捉动态耦合关系，并采用了一种几何感知的位置编码方法，以增强空间精度并实现点对点超分辨率提升。 在多个多相流案例中的实证结果表明，与现有的NO基准模型相比，IANO实现了显著的准确性改进（最高可达约10%）。此外，IANO在低数据量和噪声环境下的泛化能力更强，证实了其在基于AI的实际且高效的数据驱动多相流模拟中的实用性。
URL
https://arxiv.org/abs/2511.08625
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PGP-DiffSR: Phase-Guided Progressive Pruning for Efficient Diffusion-based Image Super-Resolution
2025-12-02 12:06:39Zhongbao Yang, Jiangxin Dong, Yazhou Yao, Jinhui Tang, Jinshan PanAbstract
Although diffusion-based models have achieved impressive results in image super-resolution, they often rely on large-scale backbones such as Stable Diffusion XL (SDXL) and Diffusion Transformers (DiT), which lead to excessive computational and memory costs during training and inference. To address this issue, we develop a lightweight diffusion method, PGP-DiffSR, by removing redundant information from diffusion models under the guidance of the phase information of inputs for efficient image super-resolution. We first identify the intra-block redundancy within the diffusion backbone and propose a progressive pruning approach that removes redundant blocks while reserving restoration capability. We note that the phase information of the restored images produced by the pruned diffusion model is not well estimated. To solve this problem, we propose a phase-exchange adapter module that explores the phase information of the inputs to guide the pruned diffusion model for better restoration performance. We formulate the progressive pruning approach and the phase-exchange adapter module into a unified model. Extensive experiments demonstrate that our method achieves competitive restoration quality while significantly reducing computational load and memory consumption. The code is available at this https URL.
Abstract (translated)
尽管基于扩散的模型在图像超分辨率方面取得了令人印象深刻的结果，但它们通常依赖于大规模的基础架构，如Stable Diffusion XL（SDXL）和Diffusion Transformers（DiT），这会导致训练和推理过程中的计算和内存成本过高。为了解决这个问题，我们开发了一种轻量级的扩散方法PGP-DiffSR，在输入数据相位信息的指导下从扩散模型中去除冗余信息，从而实现高效的图像超分辨率处理。 首先，我们识别出扩散基础架构内的块间冗余，并提出一种渐进式剪枝的方法，这种方法在移除冗余块的同时保留了恢复能力。注意到由剪枝后的扩散模型生成的重建图像的相位信息估计不佳。为了解决这一问题，我们提出了一个相位交换适配器模块，该模块利用输入数据的相位信息来引导剪枝后的扩散模型以获得更好的恢复性能。 我们将渐进式剪枝方法和相位交换适配器模块统一集成到一个模型中。广泛的实验表明，我们的方法在显著降低计算负载和内存消耗的同时，实现了竞争性的恢复质量。代码可在[此链接](https://这个URL被原作者省略了，请访问原文获取准确的链接)获取。
URL
https://arxiv.org/abs/2512.02681
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OmniPerson: Unified Identity-Preserving Pedestrian Generation
2025-12-02 09:24:34Changxiao Ma, Chao Yuan, Xincheng Shi, Yuzhuo Ma, Yongfei Zhang, Longkun Zhou, Yujia Zhang, Shangze Li, Yifan XuAbstract
Person re-identification (ReID) suffers from a lack of large-scale high-quality training data due to challenges in data privacy and annotation costs. While previous approaches have explored pedestrian generation for data augmentation, they often fail to ensure identity consistency and suffer from insufficient controllability, thereby limiting their effectiveness in dataset augmentation. To address this, We introduce OmniPerson, the first unified identity-preserving pedestrian generation pipeline for visible/infrared image/video ReID tasks. Our contributions are threefold: 1) We proposed OmniPerson, a unified generation model, offering holistic and fine-grained control over all key pedestrian attributes. Supporting RGB/IR modality image/video generation with any number of reference images, two kinds of person poses, and text. Also including RGB-to-IR transfer and image super-resolution abilities.2) We designed Multi-Refer Fuser for robust identity preservation with any number of reference images as input, making OmniPerson could distill a unified identity from a set of multi-view reference images, ensuring our generated pedestrians achieve high-fidelity pedestrian generation.3) We introduce PersonSyn, the first large-scale dataset for multi-reference, controllable pedestrian generation, and present its automated curation pipeline which transforms public, ID-only ReID benchmarks into a richly annotated resource with the dense, multi-modal supervision required for this task. Experimental results demonstrate that OmniPerson achieves SoTA in pedestrian generation, excelling in both visual fidelity and identity consistency. Furthermore, augmenting existing datasets with our generated data consistently improves the performance of ReID models. We will open-source the full codebase, pretrained model, and the PersonSyn dataset.
Abstract (translated)
人员重识别（ReID）面临缺乏大规模高质量训练数据的问题，这主要是由于数据隐私和标注成本的挑战。虽然之前的方法探索了行人生成以进行数据增强，但它们往往无法确保身份一致性，并且控制力不足，从而限制了在数据集扩充中的有效性。为解决这一问题，我们引入了OmniPerson，这是第一个统一的身份保持型行人生成管道，适用于可见光/红外图像和视频ReID任务。 我们的贡献有三点： 1. 我们提出了OmniPerson，一个统一的生成模型，提供对所有关键行人属性的整体性和细致控制。支持RGB/IR模态图像/视频生成，并可以使用任意数量的参考图片、两种类型的人体姿态以及文本作为输入。此外还具备从RGB到IR转换和图像超分辨率能力。 2. 我们设计了多参照融合器（Multi-Refer Fuser），用于在输入任意数量的参考图片时进行稳健的身份保持，使OmniPerson能够从一组多视角参考图集中提取统一身份，并确保我们生成的行人具有高保真度的人行者生成效果。 3. 我们引入了PersonSyn，这是第一个大规模数据集，专门针对可引用的可控性行人生成。还介绍了其自动编目管道，该管道可以将仅凭ID的公共ReID基准转换为完成密集且多模态监督所需的任务丰富注释资源。 实验结果表明，OmniPerson在行人的生成方面达到了最新的技术水平，在视觉保真度和身份一致性上都表现出色。此外，通过使用我们生成的数据增强现有数据集，能够持续提升ReID模型的性能。我们将开源完整的代码库、预训练模型以及PersonSyn数据集。
URL
https://arxiv.org/abs/2512.02554
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Two-Stage Vision Transformer for Image Restoration: Colorization Pretraining + Residual Upsampling
2025-12-02 08:10:55Aditya Chaudhary, Prachet Dev Singh, Ankit JhaAbstract
In computer vision, Single Image Super-Resolution (SISR) is still a difficult problem. We present ViT-SR, a new technique to improve the performance of a Vision Transformer (ViT) employing a two-stage training strategy. In our method, the model learns rich, generalizable visual representations from the data itself through a self-supervised pretraining phase on a colourization task. The pre-trained model is then adjusted for 4x super-resolution. By predicting the addition of a high-frequency residual image to an initial bicubic interpolation, this design simplifies residual learning. ViT-SR, trained and evaluated on the DIV2K benchmark dataset, achieves an impressive SSIM of 0.712 and PSNR of 22.90 dB. These results demonstrate the efficacy of our two-stage approach and highlight the potential of self-supervised pre-training for complex image restoration tasks. Further improvements may be possible with larger ViT architectures or alternative pretext tasks.
Abstract (translated)
在计算机视觉领域，单图像超分辨率（SISR）仍然是一个具有挑战性的问题。我们提出了一种名为ViT-SR的新技术，该技术通过两阶段训练策略来提高Vision Transformer (ViT) 的性能。我们的方法中，模型通过在着色任务上的自我监督预训练阶段从数据本身学习丰富的、可泛化的视觉表示。然后将预训练的模型调整为进行4倍超分辨率处理。通过预测向初始双三次插值图像添加高频残差图像，这种设计简化了残差学习的过程。 在DIV2K基准数据集上进行训练和评估后，ViT-SR取得了令人印象深刻的SSIM（结构相似性指数）0.712 和 PSNR（峰值信噪比）22.90 dB 的结果。这些成果展示了我们两阶段方法的有效性，并突显了自我监督预训练在复杂图像恢复任务中的潜力。通过使用更大规模的ViT架构或替代前置任务，可能进一步改进性能。
URL
https://arxiv.org/abs/2512.02512
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Progressive Image Restoration via Text-Conditioned Video Generation
2025-12-01 23:37:51Peng Kang, Xijun Wang, Yu YuanAbstract
Recent text-to-video models have demonstrated strong temporal generation capabilities, yet their potential for image restoration remains underexplored. In this work, we repurpose CogVideo for progressive visual restoration tasks by fine-tuning it to generate restoration trajectories rather than natural video motion. Specifically, we construct synthetic datasets for super-resolution, deblurring, and low-light enhancement, where each sample depicts a gradual transition from degraded to clean frames. Two prompting strategies are compared: a uniform text prompt shared across all samples, and a scene-specific prompting scheme generated via LLaVA multi-modal LLM and refined with ChatGPT. Our fine-tuned model learns to associate temporal progression with restoration quality, producing sequences that improve perceptual metrics such as PSNR, SSIM, and LPIPS across frames. Extensive experiments show that CogVideo effectively restores spatial detail and illumination consistency while maintaining temporal coherence. Moreover, the model generalizes to real-world scenarios on the ReLoBlur dataset without additional training, demonstrating strong zero-shot robustness and interpretability through temporal restoration.
Abstract (translated)
最近的文本到视频模型展示了强大的时间生成能力，但它们在图像恢复方面的潜力尚未得到充分探索。在这项工作中，我们将CogVideo重新用于渐进式视觉恢复任务，通过微调使其生成恢复轨迹而非自然视频运动。具体来说，我们构建了超分辨率、去模糊和低光增强的合成数据集，在这些数据集中，每个样本都展示了从降质图像到清晰图像的逐步过渡。我们比较了两种提示策略：一种是在所有样本中共享的统一文本提示，另一种是通过LLaVA多模态大语言模型生成并由ChatGPT优化的情境特定提示方案。 经过微调后，我们的模型学会了将时间进程与恢复质量关联起来，在帧之间改善如峰值信噪比（PSNR）、结构相似性指数度量（SSIM）和感知线性判别分析（LPIPS）等感知指标。广泛的实验表明，CogVideo有效地恢复了空间细节和光照一致性，并保持了时间连贯性。此外，该模型在无需额外训练的情况下，在ReLoBlur数据集上的真实场景中表现出强大的零样本鲁棒性和通过时间恢复的可解释性。
URL
https://arxiv.org/abs/2512.02273
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SplatSuRe: Selective Super-Resolution for Multi-view Consistent 3D Gaussian Splatting
2025-12-01 20:08:39Pranav Asthana, Alex Hanson, Allen Tu, Tom Goldstein, Matthias Zwicker, Amitabh VarshneyAbstract
3D Gaussian Splatting (3DGS) enables high-quality novel view synthesis, motivating interest in generating higher-resolution renders than those available during training. A natural strategy is to apply super-resolution (SR) to low-resolution (LR) input views, but independently enhancing each image introduces multi-view inconsistencies, leading to blurry renders. Prior methods attempt to mitigate these inconsistencies through learned neural components, temporally consistent video priors, or joint optimization on LR and SR views, but all uniformly apply SR across every image. In contrast, our key insight is that close-up LR views may contain high-frequency information for regions also captured in more distant views, and that we can use the camera pose relative to scene geometry to inform where to add SR content. Building from this insight, we propose SplatSuRe, a method that selectively applies SR content only in undersampled regions lacking high-frequency supervision, yielding sharper and more consistent results. Across Tanks & Temples, Deep Blending and Mip-NeRF 360, our approach surpasses baselines in both fidelity and perceptual quality. Notably, our gains are most significant in localized foreground regions where higher detail is desired.
Abstract (translated)
3D高斯点阵（3D Gaussian Splatting，简称3DGS）能够实现高质量的新视角合成，这激发了人们对生成比训练时更高分辨率渲染图的兴趣。一种自然的策略是将超分辨率技术应用于低分辨率输入视图，但独立地增强每一张图像会引入多视图不一致性，从而导致模糊的渲染效果。先前的方法试图通过学习神经网络组件、时间上一致的视频先验知识或同时对低分辨率和高分辨率视图进行联合优化来缓解这些不一致性问题，但所有这些方法都统一地在每一幅图像中应用超分辨率技术。 相比之下，我们的关键见解是：近距离的低分辨率视角可能包含高频信息，而这些信息同样可以在更远距离的视角中找到。我们可以通过相机相对于场景几何的位置来确定在哪里增加超分辨率内容。基于这一见解，我们提出了SplatSuRe方法，该方法仅在缺少高频监督的信息不足区域选择性地应用超分辨率内容，从而产生更加清晰和一致的结果。 我们的方法在Tanks & Temples、Deep Blending 和 Mip-NeRF 360 数据集上均超越了基线，在保真度和感知质量方面表现更佳。特别值得注意的是，我们在前景局部区域取得了最大的收益，这些地方需要更高的细节水平。
URL
https://arxiv.org/abs/2512.02172
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FRAMER: Frequency-Aligned Self-Distillation with Adaptive Modulation Leveraging Diffusion Priors for Real-World Image Super-Resolution
2025-12-01 08:09:05Seungho Choi, Jeahun Sung, Jihyong OhAbstract
Real-image super-resolution (Real-ISR) seeks to recover HR images from LR inputs with mixed, unknown degradations. While diffusion models surpass GANs in perceptual quality, they under-reconstruct high-frequency (HF) details due to a low-frequency (LF) bias and a depth-wise "low-first, high-later" hierarchy. We introduce FRAMER, a plug-and-play training scheme that exploits diffusion priors without changing the backbone or inference. At each denoising step, the final-layer feature map teaches all intermediate layers. Teacher and student feature maps are decomposed into LF/HF bands via FFT masks to align supervision with the model's internal frequency hierarchy. For LF, an Intra Contrastive Loss (IntraCL) stabilizes globally shared structure. For HF, an Inter Contrastive Loss (InterCL) sharpens instance-specific details using random-layer and in-batch negatives. Two adaptive modulators, Frequency-based Adaptive Weight (FAW) and Frequency-based Alignment Modulation (FAM), reweight per-layer LF/HF signals and gate distillation by current similarity. Across U-Net and DiT backbones (e.g., Stable Diffusion 2, 3), FRAMER consistently improves PSNR/SSIM and perceptual metrics (LPIPS, NIQE, MANIQA, MUSIQ). Ablations validate the final-layer teacher and random-layer negatives.
Abstract (translated)
真实图像超分辨率（Real-ISR）旨在从含有混合未知退化的低分辨率输入中恢复高分辨率图像。虽然扩散模型在感知质量上超越了生成对抗网络（GANs），但由于它们对低频信号的偏好和深度方向上的“先低后高”层次结构，这些模型难以准确重构高频细节。我们引入了一种名为FRAMER的即插即用训练方案，该方案利用了扩散模型的先验知识，而不改变主干网络或推断过程。 在每个去噪步骤中，最终层特征图会指导所有中间层。教师和学生特征图通过FFT掩码被分解成低频（LF）/高频（HF）带，以确保监督与模型内部频率层次结构对齐。对于低频信号，我们引入了一个内对比损失（Intra Contrastive Loss, IntraCL），它有助于稳定全局共享的结构；而对于高频信号，则使用随机层和批次内的负样本来增强特定实例的细节，即交互式对比损失（Inter Contrastive Loss, InterCL）。 此外，还引入了两种自适应调制器：基于频率的自适应权重（Frequency-based Adaptive Weight, FAW）和基于频率对齐调节（Frequency-based Alignment Modulation, FAM）。FAW重新调整每个层次中的低频/高频信号的权重，而FAM通过当前相似度来控制知识蒸馏的过程。 在U-Net和DiT主干网络（如Stable Diffusion 2、3）上进行实验时，FRAMER方案在峰值信噪比（PSNR）、结构相似性指数（SSIM），以及感知质量指标（LPIPS、NIQE、MANIQA、MUSIQ）等多个方面都得到了一致的改进。消融研究验证了最终层教师和随机层负样本的有效性。 该方法通过利用扩散模型的优势并克服其固有的局限性，为图像超分辨率领域提供了一种新的训练策略，具有广泛的应用前景。
URL
https://arxiv.org/abs/2512.01390
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HIMOSA: Efficient Remote Sensing Image Super-Resolution with Hierarchical Mixture of Sparse Attention
2025-11-29 02:00:15Yi Liu, Yi Wan, Xinyi Liu, Qiong Wu, Panwang Xia, Xuejun Huang, Yongjun ZhangAbstract
In remote sensing applications, such as disaster detection and response, real-time efficiency and model lightweighting are of critical importance. Consequently, existing remote sensing image super-resolution methods often face a trade-off between model performance and computational efficiency. In this paper, we propose a lightweight super-resolution framework for remote sensing imagery, named HIMOSA. Specifically, HIMOSA leverages the inherent redundancy in remote sensing imagery and introduces a content-aware sparse attention mechanism, enabling the model to achieve fast inference while maintaining strong reconstruction performance. Furthermore, to effectively leverage the multi-scale repetitive patterns found in remote sensing imagery, we introduce a hierarchical window expansion and reduce the computational complexity by adjusting the sparsity of the attention. Extensive experiments on multiple remote sensing datasets demonstrate that our method achieves state-of-the-art performance while maintaining computational efficiency.
Abstract (translated)
在遥感应用中，如灾害检测和响应等领域，实时效率和模型轻量化至关重要。因此，现有的遥感图像超分辨率方法往往需要在模型性能与计算效率之间做出权衡。本文提出了一种用于遥感影像的轻量级超分辨率框架，命名为HIMOSA。具体而言，HIMOSA利用了遥感影像中存在的内在冗余，并引入了一种基于内容感知的稀疏注意力机制，使模型能够在保持强大重建性能的同时实现快速推理。此外，为了有效地利用遥感图像中发现的多尺度重复模式，我们提出了层次窗口扩展方法，并通过调整注意力的稀疏性来降低计算复杂度。在多个遥感数据集上的大量实验表明，我们的方法在保持计算效率的前提下实现了最先进的性能。
URL
https://arxiv.org/abs/2512.00275
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Breaking Scale Anchoring: Frequency Representation Learning for Accurate High-Resolution Inference from Low-Resolution Training
2025-11-28 09:52:37Wenshuo Wang, Fan ZhangAbstract
Zero-Shot Super-Resolution Spatiotemporal Forecasting requires a deep learning model to be trained on low-resolution data and deployed for inference on high-resolution. Existing studies consider maintaining similar error across different resolutions as indicative of successful multi-resolution generalization. However, deep learning models serving as alternatives to numerical solvers should reduce error as resolution increases. The fundamental limitation is, the upper bound of physical law frequencies that low-resolution data can represent is constrained by its Nyquist frequency, making it difficult for models to process signals containing unseen frequency components during high-resolution inference. This results in errors being anchored at low resolution, incorrectly interpreted as successful generalization. We define this fundamental phenomenon as a new problem distinct from existing issues: Scale Anchoring. Therefore, we propose architecture-agnostic Frequency Representation Learning. It alleviates Scale Anchoring through resolution-aligned frequency representations and spectral consistency training: on grids with higher Nyquist frequencies, the frequency response in high-frequency bands of FRL-enhanced variants is more stable. This allows errors to decrease with resolution and significantly outperform baselines within our task and resolution range, while incurring only modest computational overhead.
Abstract (translated)
零样本超分辨率时空预测要求深度学习模型在低分辨率数据上进行训练，并部署到高分辨率数据上进行推理。现有的研究认为，在不同分辨率下保持相似的误差是多分辨率泛化成功的标志。然而，作为数值求解器替代方案的深度学习模型应该随着分辨率的提高而减少误差。基本限制在于，低分辨率数据所能表示的物理定律频率上限由其奈奎斯特频率决定，这使得模型难以处理在高分辨率推理中出现的新频谱成分信号。这导致了错误被锚定在低分辨率上，并被误认为是成功的泛化。我们定义这一根本现象为一个新的问题，即尺度锁定（Scale Anchoring）。 因此，我们提出了一种架构无关的频率表示学习（Frequency Representation Learning, FRL）。通过使用与分辨率对齐的频率表示和谱一致性训练，在具有更高奈奎斯特频率的网格上，FRL增强模型在高频带中的频率响应更为稳定。这使得误差随着分辨率提高而减少，并且在我们设定的任务和分辨率范围内大幅超越基线性能，同时仅带来适度的计算开销。
URL
https://arxiv.org/abs/2512.05132
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INSIGHT: An Interpretable Neural Vision-Language Framework for Reasoning of Generative Artifacts
2025-11-27 11:43:50Anshul BagariaAbstract
The growing realism of AI-generated images produced by recent GAN and diffusion models has intensified concerns over the reliability of visual media. Yet, despite notable progress in deepfake detection, current forensic systems degrade sharply under real-world conditions such as severe downsampling, compression, and cross-domain distribution shifts. Moreover, most detectors operate as opaque classifiers, offering little insight into why an image is flagged as synthetic, undermining trust and hindering adoption in high-stakes settings. We introduce INSIGHT (Interpretable Neural Semantic and Image-based Generative-forensic Hallucination Tracing), a unified multimodal framework for robust detection and transparent explanation of AI-generated images, even at extremely low resolutions (16x16 - 64x64). INSIGHT combines hierarchical super-resolution for amplifying subtle forensic cues without inducing misleading artifacts, Grad-CAM driven multi-scale localization to reveal spatial regions indicative of generative patterns, and CLIP-guided semantic alignment to map visual anomalies to human-interpretable descriptors. A vision-language model is then prompted using a structured ReAct + Chain-of-Thought protocol to produce consistent, fine-grained explanations, verified through a dual-stage G-Eval + LLM-as-a-judge pipeline to minimize hallucinations and ensure factuality. Across diverse domains, including animals, vehicles, and abstract synthetic scenes, INSIGHT substantially improves both detection robustness and explanation quality under extreme degradation, outperforming prior detectors and black-box VLM baselines. Our results highlight a practical path toward transparent, reliable AI-generated image forensics and establish INSIGHT as a step forward in trustworthy multimodal content verification.
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https://arxiv.org/abs/2511.22351
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IE-SRGS: An Internal-External Knowledge Fusion Framework for High-Fidelity 3D Gaussian Splatting Super-Resolution
2025-11-27 08:57:42Xiang Feng, Tieshi Zhong, Shuo Chang, Weiliu Wang, Chengkai Wang, Yifei Chen, Yuhe Wang, Zhenzhong Kuang, Xuefei Yin, Yanming ZhuAbstract
Reconstructing high-resolution (HR) 3D Gaussian Splatting (3DGS) models from low-resolution (LR) inputs remains challenging due to the lack of fine-grained textures and geometry. Existing methods typically rely on pre-trained 2D super-resolution (2DSR) models to enhance textures, but suffer from 3D Gaussian ambiguity arising from cross-view inconsistencies and domain gaps inherent in 2DSR models. We propose IE-SRGS, a novel 3DGS SR paradigm that addresses this issue by jointly leveraging the complementary strengths of external 2DSR priors and internal 3DGS features. Specifically, we use 2DSR and depth estimation models to generate HR images and depth maps as external knowledge, and employ multi-scale 3DGS models to produce cross-view consistent, domain-adaptive counterparts as internal knowledge. A mask-guided fusion strategy is introduced to integrate these two sources and synergistically exploit their complementary strengths, effectively guiding the 3D Gaussian optimization toward high-fidelity reconstruction. Extensive experiments on both synthetic and real-world benchmarks show that IE-SRGS consistently outperforms state-of-the-art methods in both quantitative accuracy and visual fidelity.
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https://arxiv.org/abs/2511.22233
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ICM-SR: Image-Conditioned Manifold Regularization for Image Super-Resoultion
2025-11-27 03:06:21Junoh Kang, Donghun Ryu, Bohyung HanAbstract
Real world image super-resolution (Real-ISR) often leverages the powerful generative priors of text-to-image diffusion models by regularizing the output to lie on their learned manifold. However, existing methods often overlook the importance of the regularizing manifold, typically defaulting to a text-conditioned manifold. This approach suffers from two key limitations. Conceptually, it is misaligned with the Real-ISR task, which is to generate high quality (HQ) images directly tied to the low quality (LQ) images. Practically, the teacher model often reconstructs images with color distortions and blurred edges, indicating a flawed generative prior for this task. To correct these flaws and ensure conceptual alignment, a more suitable manifold must incorporate information from the images. While the most straightforward approach is to condition directly on the raw input images, their high information densities make the regularization process numerically unstable. To resolve this, we propose image-conditioned manifold regularization (ICM), a method that regularizes the output towards a manifold conditioned on the sparse yet essential structural information: a combination of colormap and Canny edges. ICM provides a task-aligned and stable regularization signal, thereby avoiding the instability of dense-conditioning and enhancing the final super-resolution quality. Our experiments confirm that the proposed regularization significantly enhances super-resolution performance, particularly in perceptual quality, demonstrating its effectiveness for real-world applications. We will release the source code of our work for reproducibility.
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https://arxiv.org/abs/2511.22048
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GaINeR: Geometry-Aware Implicit Network Representation
2025-11-25 23:37:54Weronika Jakubowska, Miko{\l}aj Zieli\'nski, Rafa{\l} Tobiasz, Krzysztof Byrski, Maciej Zi\k{e}ba, Dominik Belter, Przemys{\l}aw SpurekAbstract
Implicit Neural Representations (INRs) have become an essential tool for modeling continuous 2D images, enabling high-fidelity reconstruction, super-resolution, and compression. Popular architectures such as SIREN, WIRE, and FINER demonstrate the potential of INR for capturing fine-grained image details. However, traditional INRs often lack explicit geometric structure and have limited capabilities for local editing or integration with physical simulation, restricting their applicability in dynamic or interactive settings. To address these limitations, we propose GaINeR: Geometry-Aware Implicit Network Representation, a novel framework for 2D images that combines trainable Gaussian distributions with a neural network-based INR. For a given image coordinate, the model retrieves the K nearest Gaussians, aggregates distance-weighted embeddings, and predicts the RGB value via a neural network. This design enables continuous image representation, interpretable geometric structure, and flexible local editing, providing a foundation for physically aware and interactive image manipulation. The official implementation of our method is publicly available at this https URL.
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https://arxiv.org/abs/2511.20924
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Restora-Flow: Mask-Guided Image Restoration with Flow Matching
2025-11-25 10:22:26Arnela Hadzic, Franz Thaler, Lea Bogensperger, Simon Johannes Joham, Martin UrschlerAbstract
Flow matching has emerged as a promising generative approach that addresses the lengthy sampling times associated with state-of-the-art diffusion models and enables a more flexible trajectory design, while maintaining high-quality image generation. This capability makes it suitable as a generative prior for image restoration tasks. Although current methods leveraging flow models have shown promising results in restoration, some still suffer from long processing times or produce over-smoothed results. To address these challenges, we introduce Restora-Flow, a training-free method that guides flow matching sampling by a degradation mask and incorporates a trajectory correction mechanism to enforce consistency with degraded inputs. We evaluate our approach on both natural and medical datasets across several image restoration tasks involving a mask-based degradation, i.e., inpainting, super-resolution and denoising. We show superior perceptual quality and processing time compared to diffusion and flow matching-based reference methods.
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https://arxiv.org/abs/2511.20152
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History-Augmented Contrastive Meta-Learning for Unsupervised Blind Super-Resolution of Planetary Remote Sensing Images
2025-11-25 08:13:15Huijia Zhao, Jie Lu, Yunqing Jiang, Xiao-Ping Lu, Kaichang DiAbstract
Planetary remote sensing images are affected by diverse and unknown degradations caused by imaging environments and hardware constraints. These factors limit image quality and hinder supervised blind super-resolution due to the lack of ground-truth images. This work presents History-Augmented Contrastive Blind Super-Resolution (HACBSR), an unsupervised framework for blind super-resolution that operates without ground-truth images and external kernel priors. HACBSR comprises two components: (1) a contrastive kernel sampling mechanism with kernel similarity control to mitigate distribution bias from Gaussian sampling, and (2) a history-augmented contrastive learning that uses historical models to generate negative samples to enable less greedy optimization and to induce strong convexity without ground-truth. A convergence analysis of the history-augmented contrastive learning is given in the Appendix. To support evaluation in planetary applications, we introduce Ceres-50, a dataset with diverse geological features simulated degradation patterns. Experiments show that HACBSR achieves competitive performance compared with state-of-the-art unsupervised methods across multiple upscaling factors. The code is available at this https URL, and the dataset is available at this https URL.
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URL
https://arxiv.org/abs/2511.20045
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3M-TI: High-Quality Mobile Thermal Imaging via Calibration-free Multi-Camera Cross-Modal Diffusion
2025-11-24 13:48:47Minchong Chen, Xiaoyun Yuan, Junzhe Wan, Jianing Zhang, Jun ZhangAbstract
The miniaturization of thermal sensors for mobile platforms inherently limits their spatial resolution and textural fidelity, leading to blurry and less informative images. Existing thermal super-resolution (SR) methods can be grouped into single-image and RGB-guided approaches: the former struggles to recover fine structures from limited information, while the latter relies on accurate and laborious cross-camera calibration, which hinders practical deployment and robustness. Here, we propose 3M-TI, a calibration-free Multi-camera cross-Modality diffusion framework for Mobile Thermal Imaging. At its core, 3M-TI integrates a cross-modal self-attention module (CSM) into the diffusion UNet, replacing the original self-attention layers to adaptively align thermal and RGB features throughout the denoising process, without requiring explicit camera calibration. This design enables the diffusion network to leverage its generative prior to enhance spatial resolution, structural fidelity, and texture detail in the super-resolved thermal images. Extensive evaluations on real-world mobile thermal cameras and public benchmarks validate our superior performance, achieving state-of-the-art results in both visual quality and quantitative metrics. More importantly, the thermal images enhanced by 3M-TI lead to substantial gains in critical downstream tasks like object detection and segmentation, underscoring its practical value for robust mobile thermal perception systems. More materials: this https URL.
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URL
https://arxiv.org/abs/2511.19117
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MFmamba: A Multi-function Network for Panchromatic Image Resolution Restoration Based on State-Space Model
2025-11-24 08:44:04Qian Jiang, Qianqian Wang, Xin Jin, Michal Wozniak, Shaowen Yao, Wei ZhouAbstract
Remote sensing images are becoming increasingly widespread in military, earth resource exploration. Because of the limitation of a single sensor, we can obtain high spatial resolution grayscale panchromatic (PAN) images and low spatial resolution color multispectral (MS) images. Therefore, an important issue is to obtain a color image with high spatial resolution when there is only a PAN image at the input. The existing methods improve spatial resolution using super-resolution (SR) technology and spectral recovery using colorization technology. However, the SR technique cannot improve the spectral resolution, and the colorization technique cannot improve the spatial resolution. Moreover, the pansharpening method needs two registered inputs and can not achieve SR. As a result, an integrated approach is expected. To solve the above problems, we designed a novel multi-function model (MFmamba) to realize the tasks of SR, spectral recovery, joint SR and spectral recovery through three different inputs. Firstly, MFmamba utilizes UNet++ as the backbone, and a Mamba Upsample Block (MUB) is combined with UNet++. Secondly, a Dual Pool Attention (DPA) is designed to replace the skip connection in UNet++. Finally, a Multi-scale Hybrid Cross Block (MHCB) is proposed for initial feature extraction. Many experiments show that MFmamba is competitive in evaluation metrics and visual results and performs well in the three tasks when only the input PAN image is used.
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URL
https://arxiv.org/abs/2511.18888
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HunyuanVideo 1.5 Technical Report
2025-11-24 08:22:07Bing Wu, Chang Zou, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Jack Peng, Jianbing Wu, Jiangfeng Xiong, Jie Jiang, Linus, Patrol, Peizhen Zhang, Peng Chen, Penghao Zhao, Qi Tian, Songtao Liu, Weijie Kong, Weiyan Wang, Xiao He, Xin Li, Xinchi Deng, Xuefei Zhe, Yang Li, Yanxin Long, Yuanbo Peng, Yue Wu, Yuhong Liu, Zhenyu Wang, Zuozhuo Dai, Bo Peng, Coopers Li, Gu Gong, Guojian Xiao, Jiahe Tian, Jiaxin Lin, Jie Liu, Jihong Zhang, Jiesong Lian, Kaihang Pan, Lei Wang, Lin Niu, Mingtao Chen, Mingyang Chen, Mingzhe Zheng, Miles Yang, Qiangqiang Hu, Qi Yang, Qiuyong Xiao, Runzhou Wu, Ryan Xu, Rui Yuan, Shanshan Sang, Shisheng Huang, Siruis Gong, Shuo Huang, Weiting Guo, Xiang Yuan, Xiaojia Chen, Xiawei Hu, Wenzhi Sun, Xiele Wu, Xianshun Ren, Xiaoyan Yuan, Xiaoyue Mi, Yepeng Zhang, Yifu Sun, Yiting Lu, Yitong Li, You Huang, Yu Tang, Yixuan Li, Yuhang Deng, Yuan Zhou, Zhichao Hu, Zhiguang Liu, Zhihe Yang, Zilin Yang, Zhenzhi Lu, Zixiang Zhou, Zhao ZhongAbstract
We present HunyuanVideo 1.5, a lightweight yet powerful open-source video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture featuring selective and sliding tile attention (SSTA), enhanced bilingual understanding through glyph-aware text encoding, progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and this http URL experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source video generation models. By releasing the code and model weights, we provide the community with a high-performance foundation that lowers the barrier to video creation and research, making advanced video generation accessible to a broader audience. All open-source assets are publicly available at this https URL.
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URL
https://arxiv.org/abs/2511.18870
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STCDiT: Spatio-Temporally Consistent Diffusion Transformer for High-Quality Video Super-Resolution
2025-11-24 05:37:23Junyang Chen, Jiangxin Dong, Long Sun, Yixin Yang, Jinshan PanAbstract
We present STCDiT, a video super-resolution framework built upon a pre-trained video diffusion model, aiming to restore structurally faithful and temporally stable videos from degraded inputs, even under complex camera motions. The main challenges lie in maintaining temporal stability during reconstruction and preserving structural fidelity during generation. To address these challenges, we first develop a motion-aware VAE reconstruction method that performs segment-wise reconstruction, with each segment clip exhibiting uniform motion characteristic, thereby effectively handling videos with complex camera motions. Moreover, we observe that the first-frame latent extracted by the VAE encoder in each clip, termed the anchor-frame latent, remains unaffected by temporal compression and retains richer spatial structural information than subsequent frame latents. We further develop an anchor-frame guidance approach that leverages structural information from anchor frames to constrain the generation process and improve structural fidelity of video features. Coupling these two designs enables the video diffusion model to achieve high-quality video super-resolution. Extensive experiments show that STCDiT outperforms state-of-the-art methods in terms of structural fidelity and temporal consistency.
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URL
https://arxiv.org/abs/2511.18786
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Neural Geometry Image-Based Representations with Optimal Transport
2025-11-24 01:43:19Xiang Gao, Yuanpeng Liu, Xinmu Wang, Jiazhi Li, Minghao Guo, Yu Guo, Xiyun Song, Heather Yu, Zhiqiang Lao, Xianfeng David GuAbstract
Neural representations for 3D meshes are emerging as an effective solution for compact storage and efficient processing. Existing methods often rely on neural overfitting, where a coarse mesh is stored and progressively refined through multiple decoder networks. While this can restore high-quality surfaces, it is computationally expensive due to successive decoding passes and the irregular structure of mesh data. In contrast, images have a regular structure that enables powerful super-resolution and restoration frameworks, but applying these advantages to meshes is difficult because their irregular connectivity demands complex encoder-decoder architectures. Our key insight is that a geometry image-based representation transforms irregular meshes into a regular image grid, making efficient image-based neural processing directly applicable. Building on this idea, we introduce our neural geometry image-based representation, which is decoder-free, storage-efficient, and naturally suited for neural processing. It stores a low-resolution geometry-image mipmap of the surface, from which high-quality meshes are restored in a single forward pass. To construct geometry images, we leverage Optimal Transport (OT), which resolves oversampling in flat regions and undersampling in feature-rich regions, and enables continuous levels of detail (LoD) through geometry-image mipmapping. Experimental results demonstrate state-of-the-art storage efficiency and restoration accuracy, measured by compression ratio (CR), Chamfer distance (CD), and Hausdorff distance (HD).
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URL
https://arxiv.org/abs/2511.18679
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