Super_Resolution
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A General-Purpose Diversified 2D Seismic Image Dataset from NAMSS
2026-01-27 14:01:29Lucas de Magalh\~aes Araujo, Ot\'avio Oliveira Napoli, Sandra Avila, Edson BorinAbstract
We introduce the Unicamp-NAMSS dataset, a large, diverse, and geographically distributed collection of migrated 2D seismic sections designed to support modern machine learning research in geophysics. We constructed the dataset from the National Archive of Marine Seismic Surveys (NAMSS), which contains decades of publicly available marine seismic data acquired across multiple regions, acquisition conditions, and geological settings. After a comprehensive collection and filtering process, we obtained 2588 cleaned and standardized seismic sections from 122 survey areas, covering a wide range of vertical and horizontal sampling characteristics. To ensure reliable experimentation, we balanced the dataset so that no survey dominates the distribution, and partitioned it into non-overlapping macro-regions for training, validation, and testing. This region-disjoint split allows robust evaluation of generalization to unseen geological and acquisition conditions. We validated the dataset through quantitative and embedding-space analyses using both convolutional and transformer-based models. These analyses showed that Unicamp-NAMSS exhibits substantial variability within and across regions, while maintaining coherent structure across acquisition macro-region and survey types. Comparisons with widely used interpretation datasets (Parihaka and F3 Block) further demonstrated that Unicamp-NAMSS covers a broader portion of the seismic appearance space, making it a strong candidate for machine learning model pretraining. The dataset, therefore, provides a valuable resource for machine learning tasks, including self-supervised representation learning, transfer learning, benchmarking supervised tasks such as super-resolution or attribute prediction, and studying domain adaptation in seismic interpretation.
Abstract (translated)
我们介绍了Unicamp-NAMSS数据集，这是一个大型、多样且地理分布广泛的数据集合，包含迁移的二维地震剖面，旨在支持地球物理学领域的现代机器学习研究。该数据集由国家海洋地震调查档案（NAMSS）构建而成，NAMSS包含了数十年来在多个地区和地质条件下获得并公开的海上地震数据。 经过全面收集和过滤过程后，我们从122个调查区域中获得了2588份清洗和标准化后的地震剖面，涵盖了广泛的垂直和水平采样特性。为了确保可靠的实验结果，我们将该数据集进行了平衡处理，以保证任何单一调查都不会占据主导地位，并将其划分成互不重叠的宏区用于训练、验证和测试。这种区域分离的分割方式允许对模型在未见过地质和获取条件下的泛化能力进行稳健评估。 我们使用卷积和基于Transformer的模型通过定量分析及嵌入空间分析对该数据集进行了验证，结果显示Unicamp-NAMSS在不同地区之间以及各个内部都表现出显著的变化性，并且在整个采集宏区和调查类型上保持了连贯的结构。与广泛使用的解释数据集（Parihaka和F3区块）进行对比进一步表明，Unicamp-NAMSS覆盖了更广泛的地震外观空间部分，使其成为机器学习模型预训练的一个强有力候选者。 因此，该数据集为包括自我监督表征学习、迁移学习、基准测试如超分辨率或属性预测任务以及研究地震解释中的领域适应性在内的各种机器学习任务提供了一个宝贵的资源。
URL
https://arxiv.org/abs/2602.04890
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GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution
2026-01-27 03:43:39Yongsong Huang, Tzu-Hsuan Peng, Tomo Miyazaki, Xiaofeng Liu, Chun-Ting Chou, Ai-Chun Pang, Shinichiro OmachiAbstract
Low-light image super-resolution (LLSR) is a challenging task due to the coupled degradation of low resolution and poor illumination. To address this, we propose the Guided Texture and Feature Modulation Network (GTFMN), a novel framework that decouples the LLSR task into two sub-problems: illumination estimation and texture restoration. First, our network employs a dedicated Illumination Stream whose purpose is to predict a spatially varying illumination map that accurately captures lighting distribution. Further, this map is utilized as an explicit guide within our novel Illumination Guided Modulation Block (IGM Block) to dynamically modulate features in the Texture Stream. This mechanism achieves spatially adaptive restoration, enabling the network to intensify enhancement in poorly lit regions while preserving details in well-exposed areas. Extensive experiments demonstrate that GTFMN achieves the best performance among competing methods on the OmniNormal5 and OmniNormal15 datasets, outperforming them in both quantitative metrics and visual quality.
Abstract (translated)
URL
https://arxiv.org/abs/2601.19157
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Implicit Neural Representation-Based Continuous Single Image Super Resolution: An Empirical Study
2026-01-25 07:09:20Tayyab Nasir, Daochang Liu, Ajmal MianAbstract
Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR). To date, no empirical study has systematically examined the effectiveness of existing methods, nor investigated the effects of different training recipes, such as scaling laws, objective design, and optimization strategies. A rigorous empirical analysis is essential not only for benchmarking performance and revealing true gains but also for establishing the current state of ASSR, identifying saturation limits, and highlighting promising directions. We fill this gap by comparing existing techniques across diverse settings and presenting aggregated performance results on multiple image quality metrics. We contribute a unified framework and code repository to facilitate reproducible comparisons. Furthermore, we investigate the impact of carefully controlled training configurations on perceptual image quality and examine a new loss function that penalizes intensity variations while preserving edges, textures, and finer details during training. We conclude the following key insights that have been previously overlooked: (1) Recent, more complex INR methods provide only marginal improvements over earlier methods. (2) Model performance is strongly correlated to training configurations, a factor overlooked in prior works. (3) The proposed loss enhances texture fidelity across architectures, emphasizing the role of objective design for targeted perceptual gains. (4) Scaling laws apply to INR-based ASSR, confirming predictable gains with increased model complexity and data diversity.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17723
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Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography
2026-01-24 11:56:49Mehdi Yousefzadeh, Siavash Shirzadeh Barough, Ashkan Fakharifar, Yashar Tayyarazad, Narges Eghbali, Mohaddeseh Mozaffari, Hoda Taeb, Negar Sadat Rafiee Tabatabaee, Parsa Esfahanian, Ghazaleh Sadeghi Gohar, Amineh Safavirad, Saeideh Mazloomzadeh, Ehsan khalilipur, Armin Elahifar, Majid MalekiAbstract
X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17429
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TEXTS-Diff: TEXTS-Aware Diffusion Model for Real-World Text Image Super-Resolution
2026-01-24 07:03:41Haodong He, Xin Zhan, Yancheng Bai, Rui Lan, Lei Sun, Xiangxiang ChuAbstract
Real-world text image super-resolution aims to restore overall visual quality and text legibility in images suffering from diverse degradations and text distortions. However, the scarcity of text image data in existing datasets results in poor performance on text regions. In addition, datasets consisting of isolated text samples limit the quality of background reconstruction. To address these limitations, we construct Real-Texts, a large-scale, high-quality dataset collected from real-world images, which covers diverse scenarios and contains natural text instances in both Chinese and English. Additionally, we propose the TEXTS-Aware Diffusion Model (TEXTS-Diff) to achieve high-quality generation in both background and textual regions. This approach leverages abstract concepts to improve the understanding of textual elements within visual scenes and concrete text regions to enhance textual details. It mitigates distortions and hallucination artifacts commonly observed in text regions, while preserving high-quality visual scene fidelity. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple evaluation metrics, exhibiting superior generalization ability and text restoration accuracy in complex scenarios. All the code, model, and dataset will be released.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17340
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Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models
2026-01-23 11:25:04Maxence Noble, Gonzalo I\~naki Quintana, Benjamin Aubin, Cl\'ement ChadebecAbstract
Diffusion-based image super-resolution (SR) has recently attracted significant attention by leveraging the expressive power of large pre-trained text-to-image diffusion models (DMs). A central practical challenge is resolving the trade-off between reconstruction faithfulness and photorealism. To address inference efficiency, many recent works have explored knowledge distillation strategies specifically tailored to SR, enabling one-step diffusion-based approaches. However, these teacher-student formulations are inherently constrained by information compression, which can degrade perceptual cues such as lifelike textures and depth of field, even with high overall perceptual quality. In parallel, self-distillation DMs, known as Flow Map models, have emerged as a promising alternative for image generation tasks, enabling fast inference while preserving the expressivity and training stability of standard DMs. Building on these developments, we propose FlowMapSR, a novel diffusion-based framework for image super-resolution explicitly designed for efficient inference. Beyond adapting Flow Map models to SR, we introduce two complementary enhancements: (i) positive-negative prompting guidance, based on a generalization of classifier free-guidance paradigm to Flow Map models, and (ii) adversarial fine-tuning using Low-Rank Adaptation (LoRA). Among the considered Flow Map formulations (Eulerian, Lagrangian, and Shortcut), we find that the Shortcut variant consistently achieves the best performance when combined with these enhancements. Extensive experiments show that FlowMapSR achieves a better balance between reconstruction faithfulness and photorealism than recent state-of-the-art methods for both x4 and x8 upscaling, while maintaining competitive inference time. Notably, a single model is used for both upscaling factors, without any scale-specific conditioning or degradation-guided mechanisms.
Abstract (translated)
URL
https://arxiv.org/abs/2601.16660
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DeMark: A Query-Free Black-Box Attack on Deepfake Watermarking Defenses
2026-01-23 06:04:43Wei Song, Zhenchang Xing, Liming Zhu, Yulei Sui, Jingling XueAbstract
The rapid proliferation of realistic deepfakes has raised urgent concerns over their misuse, motivating the use of defensive watermarks in synthetic images for reliable detection and provenance tracking. However, this defense paradigm assumes such watermarks are inherently resistant to removal. We challenge this assumption with DeMark, a query-free black-box attack framework that targets defensive image watermarking schemes for deepfakes. DeMark exploits latent-space vulnerabilities in encoder-decoder watermarking models through a compressive sensing based sparsification process, suppressing watermark signals while preserving perceptual and structural realism appropriate for deepfakes. Across eight state-of-the-art watermarking schemes, DeMark reduces watermark detection accuracy from 100% to 32.9% on average while maintaining natural visual quality, outperforming existing attacks. We further evaluate three defense strategies, including image super resolution, sparse watermarking, and adversarial training, and find them largely ineffective. These results demonstrate that current encoder decoder watermarking schemes remain vulnerable to latent-space manipulations, underscoring the need for more robust watermarking methods to safeguard against deepfakes.
Abstract (translated)
URL
https://arxiv.org/abs/2601.16473
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A Cosine Network for Image Super-Resolution
2026-01-23 02:58:57Chunwei Tian, Chengyuan Zhang, Bob Zhang, Zhiwu Li, C. L. Philip Chen, David ZhangAbstract
Deep convolutional neural networks can use hierarchical information to progressively extract structural information to recover high-quality images. However, preserving the effectiveness of the obtained structural information is important in image super-resolution. In this paper, we propose a cosine network for image super-resolution (CSRNet) by improving a network architecture and optimizing the training strategy. To extract complementary homologous structural information, odd and even heterogeneous blocks are designed to enlarge the architectural differences and improve the performance of image super-resolution. Combining linear and non-linear structural information can overcome the drawback of homologous information and enhance the robustness of the obtained structural information in image super-resolution. Taking into account the local minimum of gradient descent, a cosine annealing mechanism is used to optimize the training procedure by performing warm restarts and adjusting the learning rate. Experimental results illustrate that the proposed CSRNet is competitive with state-of-the-art methods in image super-resolution.
Abstract (translated)
URL
https://arxiv.org/abs/2601.16413
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Enhancing Text-to-Image Generation via End-Edge Collaborative Hybrid Super-Resolution
2026-01-21 07:55:37Chongbin Yi, Yuxin Liang, Ziqi Zhou, Peng YangAbstract
Artificial Intelligence-Generated Content (AIGC) has made significant strides, with high-resolution text-to-image (T2I) generation becoming increasingly critical for improving users' Quality of Experience (QoE). Although resource-constrained edge computing adequately supports fast low-resolution T2I generations, achieving high-resolution output still faces the challenge of ensuring image fidelity at the cost of latency. To address this, we first investigate the performance of super-resolution (SR) methods for image enhancement, confirming a fundamental trade-off that lightweight learning-based SR struggles to recover fine details, while diffusion-based SR achieves higher fidelity at a substantial computational cost. Motivated by these observations, we propose an end-edge collaborative generation-enhancement framework. Upon receiving a T2I generation task, the system first generates a low-resolution image based on adaptively selected denoising steps and super-resolution scales at the edge side, which is then partitioned into patches and processed by a region-aware hybrid SR policy. This policy applies a diffusion-based SR model to foreground patches for detail recovery and a lightweight learning-based SR model to background patches for efficient upscaling, ultimately stitching the enhanced ones into the high-resolution image. Experiments show that our system reduces service latency by 33% compared with baselines while maintaining competitive image quality.
Abstract (translated)
人工智能生成内容（AIGC）取得了显著进展，其中高分辨率的文本到图像（T2I）生成变得越来越重要，以提升用户的体验质量（QoE）。尽管资源受限的边缘计算能够支持快速生成低分辨率的T2I输出，但要实现高质量的高分辨率图像仍面临延迟和图像保真度之间的权衡。为了解决这一问题，我们首先研究了超分辨率（SR）方法在图像增强方面的性能，发现了一个基本的折中：轻量级学习型SR难以恢复精细细节，而基于扩散的SR虽然能够达到更高的图像保真度，但计算成本较高。 鉴于这些观察结果，我们提出了一种端边协作生成与增强框架。当系统收到一个T2I生成任务时，在边缘侧根据自适应选择的去噪步骤和超分辨率尺度先生成一张低分辨率图片。接着将该图片分割成多个补丁，并采用一种区域感知混合SR策略进行处理：此策略使用基于扩散的SR模型对前景补丁进行细节恢复，同时利用轻量级学习型SR模型快速扩大背景补丁尺寸，最终将其拼接为高分辨率图像。 实验表明，与基准系统相比，我们的框架在保持竞争性图像质量的同时，能够将服务延迟降低33%。
URL
https://arxiv.org/abs/2601.14741
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Quantum Super-resolution by Adaptive Non-local Observables
2026-01-20 19:40:59Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen, Shinjae YooAbstract
Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations. Classical deep learning methods have advanced SR substantially, but require increasingly deeper networks, large datasets, and heavy computation to capture fine-grained correlations. In this work, we present the \emph{first study} to investigate quantum circuits for SR. We propose a framework based on Variational Quantum Circuits (VQCs) with \emph{Adaptive Non-Local Observable} (ANO) measurements. Unlike conventional VQCs with fixed Pauli readouts, ANO introduces trainable multi-qubit Hermitian observables, allowing the measurement process to adapt during training. This design leverages the high-dimensional Hilbert space of quantum systems and the representational structure provided by entanglement and superposition. Experiments demonstrate that ANO-VQCs achieve up to five-fold higher resolution with a relatively small model size, suggesting a promising new direction at the intersection of quantum machine learning and super-resolution.
Abstract (translated)
超分辨率（SR）的目标是从低分辨率（LR）的观测数据中重建高分辨率（HR）的数据。传统的深度学习方法已经大大推进了超分辨率技术的发展，但这些方法需要越来越深的网络、大型数据集以及大量的计算资源来捕捉细微的相关性。在这项工作中，我们首次研究了量子电路在超分辨率中的应用。我们提出了一个基于变分量子线路（VQCs）并采用自适应非局部可观测量（ANO）的框架。 与传统的具有固定泡利读出操作的VQC不同，ANO引入了可训练的多量子比特厄米算子，使得测量过程可以在训练过程中进行调整和优化。这种设计利用了量子系统的高维希尔伯特空间以及纠缠和叠加提供的表示结构。实验结果表明，ANO-VQCs在模型规模相对较小的情况下可以实现高达五倍分辨率的提升，这表明在量子机器学习与超分辨率技术交叉领域中存在一个有前景的新发展方向。
URL
https://arxiv.org/abs/2601.14433
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Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution
2026-01-20 14:53:20Samuel W. Remedios, Zhangxing Bian, Shuwen Wei, Aaron Carass, Jerry L. Prince, Blake E. DeweyAbstract
Diffusion models are the current state-of-the-art for solving inverse problems in imaging. Their impressive generative capability allows them to approximate sampling from a prior distribution, which alongside a known likelihood function permits posterior sampling without retraining the model. While recent methods have made strides in advancing the accuracy of posterior sampling, the majority focuses on single-image inverse problems. However, for modalities such as magnetic resonance imaging (MRI), it is common to acquire multiple complementary measurements, each low-resolution along a different axis. In this work, we generalize common diffusion-based inverse single-image problem solvers for multi-image super-resolution (MISR) MRI. We show that the DPS likelihood correction allows an exactly-separable gradient decomposition across independently acquired measurements, enabling MISR without constructing a joint operator, modifying the diffusion model, or increasing network function evaluations. We derive MISR versions of DPS, DMAP, DPPS, and diffusion-based PnP/ADMM, and demonstrate substantial gains over SISR across $4\times/8\times/16\times$ anisotropic degradations. Our results achieve state-of-the-art super-resolution of anisotropic MRI volumes and, critically, enable reconstruction of near-isotropic anatomy from routine 2D multi-slice acquisitions, which are otherwise highly degraded in orthogonal views.
Abstract (translated)
扩散模型目前是解决成像逆问题的最先进方法。它们令人印象深刻的生成能力允许从先验分布中近似采样，结合已知的概率函数可以无需重新训练模型就能进行后验采样。尽管最近的方法在提高后验采样的准确性方面取得了进展，但大多数研究都集中在单一图像的逆向问题上。然而，在诸如磁共振成像（MRI）等模式下，通常会获取多个互补测量值，每个测量值沿不同轴低分辨率。 在这项工作中，我们将常见的基于扩散模型解决单张图像逆向问题的方法推广到了多图超分辨率（MISR）MRI。我们展示了DPS概率修正使得在独立获取的测量之间可以精确分离梯度分解，从而能够实现无需构建联合算子、修改扩散模型或增加网络函数评估次数的情况下进行MISR。 我们推导了适用于MISR的DPS、DMAP、DPPS和基于扩散的PnP/ADMM方法，并展示了它们在$4\times/8\times/16\times$各向异性退化情况下的显著性能提升。我们的结果实现了各向异性MRI体素的超分辨率的新纪录，更重要的是，这些结果能够从常规2D多切片获取中重建近似等方向解剖结构，在正交视图下这些获取通常会严重退化。 总的来说，这项研究展示了如何通过利用扩散模型和精确的概率修正技术来解决复杂的多图像逆问题，并且在MRI超分辨率领域取得了重要的进展。
URL
https://arxiv.org/abs/2601.14030
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SHARE: A Fully Unsupervised Framework for Single Hyperspectral Image Restoration
2026-01-20 14:01:13Jiangwei Xie, Zhang Wen, Mike Davies, Dongdong ChenAbstract
Hyperspectral image (HSI) restoration is a fundamental challenge in computational imaging and computer vision. It involves ill-posed inverse problems, such as inpainting and super-resolution. Although deep learning methods have transformed the field through data-driven learning, their effectiveness hinges on access to meticulously curated ground-truth datasets. This fundamentally restricts their applicability in real-world scenarios where such data is unavailable. This paper presents SHARE (Single Hyperspectral Image Restoration with Equivariance), a fully unsupervised framework that unifies geometric equivariance principles with low-rank spectral modelling to eliminate the need for ground truth. SHARE's core concept is to exploit the intrinsic invariance of hyperspectral structures under differentiable geometric transformations (e.g. rotations and scaling) to derive self-supervision signals through equivariance consistency constraints. Our novel Dynamic Adaptive Spectral Attention (DASA) module further enhances this paradigm shift by explicitly encoding the global low-rank property of HSI and adaptively refining local spectral-spatial correlations through learnable attention mechanisms. Extensive experiments on HSI inpainting and super-resolution tasks demonstrate the effectiveness of SHARE. Our method outperforms many state-of-the-art unsupervised approaches and achieves performance comparable to that of supervised methods. We hope that our approach will shed new light on HSI restoration and broader scientific imaging scenarios. The code will be released at this https URL.
Abstract (translated)
高光谱图像（HSI）恢复是计算成像和计算机视觉领域的一个基本挑战。它涉及到如插值和超分辨率之类的病态逆问题。尽管深度学习方法通过数据驱动的学习极大地改变了该领域，但它们的有效性依赖于对精心策划的地面实况数据集的访问。这种根本性的限制阻碍了这些方法在现实世界场景中的应用，因为在这些场景中通常没有可用的数据。本文提出了SHARE（具有等变性的单高光谱图像恢复），这是一种完全无监督的框架，它将几何等变性原理与低秩光谱建模相结合，以消除对地面实况数据的需求。 SHARE的核心概念是利用在可微分几何变换（例如旋转和缩放）下的高光谱结构固有的不变性，通过等价一致性约束来推导自监督信号。我们的新颖的动态自适应光谱注意（DASA）模块进一步增强了这一范式转变，该模块明确地编码了HSI的整体低秩属性，并通过可学习的关注机制自适应地细化局部光谱-空间相关性。 在HSI插值和超分辨率任务上的大量实验展示了SHARE的有效性。我们的方法优于许多最先进的无监督方法，在性能上与有监督的方法相当。我们希望我们的方法将为高光谱图像恢复以及更广泛的科学成像场景带来新的见解。代码将在[此链接](https://example.com)发布。
URL
https://arxiv.org/abs/2601.13987
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SSPFormer: Self-Supervised Pretrained Transformer for MRI Images
2026-01-19 06:01:13Jingkai Li, Xiaoze Tian, Yuhang Shen, Jia Wang, Dianjie Lu, Guijuan Zhang, Zhuoran ZhengAbstract
The pre-trained transformer demonstrates remarkable generalization ability in natural image processing. However, directly transferring it to magnetic resonance images faces two key challenges: the inability to adapt to the specificity of medical anatomical structures and the limitations brought about by the privacy and scarcity of medical data. To address these issues, this paper proposes a Self-Supervised Pretrained Transformer (SSPFormer) for MRI images, which effectively learns domain-specific feature representations of medical images by leveraging unlabeled raw imaging data. To tackle the domain gap and data scarcity, we introduce inverse frequency projection masking, which prioritizes the reconstruction of high-frequency anatomical regions to enforce structure-aware representation learning. Simultaneously, to enhance robustness against real-world MRI artifacts, we employ frequency-weighted FFT noise enhancement that injects physiologically realistic noise into the Fourier domain. Together, these strategies enable the model to learn domain-invariant and artifact-robust features directly from raw scans. Through extensive experiments on segmentation, super-resolution, and denoising tasks, the proposed SSPFormer achieves state-of-the-art performance, fully verifying its ability to capture fine-grained MRI image fidelity and adapt to clinical application requirements.
Abstract (translated)
预训练的变压器在自然图像处理中展示了卓越的泛化能力。然而，直接将其应用于磁共振成像（MRI）时会面临两个关键挑战：一是无法适应医学解剖结构的特定性；二是由于医疗数据隐私性和稀缺性的限制带来的问题。为了解决这些问题，本文提出了一种用于MRI图像的自我监督预训练变压器（SSPFormer），该模型能够通过利用未标记的原始影像数据有效学习医学图像特有的特征表示。 为了应对领域差异和数据稀缺的问题，我们引入了逆频率投影掩码技术，优先重建高频率解剖区域，以此强化结构感知型表征学习。同时，为提高对现实世界MRI伪影的鲁棒性，我们采用了频率加权FFT噪声增强技术，在傅立叶域中注入生理真实的噪声。 通过这些策略，模型能够直接从原始扫描数据中学习到领域不变性和抗伪影特征。在分割、超分辨率和去噪任务上的广泛实验表明，所提出的SSPFormer达到了最先进的性能水平，充分验证了其捕捉精细MRI图像保真度及适应临床应用需求的能力。
URL
https://arxiv.org/abs/2601.12747
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SDCoNet: Saliency-Driven Multi-Task Collaborative Network for Remote Sensing Object Detection
2026-01-18 17:36:48Ruo Qi, Linhui Dai, Yusong Qin, Chaolei Yang, Yanshan LiAbstract
In remote sensing images, complex backgrounds, weak object signals, and small object scales make accurate detection particularly challenging, especially under low-quality imaging conditions. A common strategy is to integrate single-image super-resolution (SR) before detection; however, such serial pipelines often suffer from misaligned optimization objectives, feature redundancy, and a lack of effective interaction between SR and detection. To address these issues, we propose a Saliency-Driven multi-task Collaborative Network (SDCoNet) that couples SR and detection through implicit feature sharing while preserving task specificity. SDCoNet employs the swin transformer-based shared encoder, where hierarchical window-shifted self-attention supports cross-task feature collaboration and adaptively balances the trade-off between texture refinement and semantic representation. In addition, a multi-scale saliency prediction module produces importance scores to select key tokens, enabling focused attention on weak object regions, suppression of background clutter, and suppression of adverse features introduced by multi-task coupling. Furthermore, a gradient routing strategy is introduced to mitigate optimization conflicts. It first stabilizes detection semantics and subsequently routes SR gradients along a detection-oriented direction, enabling the framework to guide the SR branch to generate high-frequency details that are explicitly beneficial for detection. Experiments on public datasets, including NWPU VHR-10-Split, DOTAv1.5-Split, and HRSSD-Split, demonstrate that the proposed method, while maintaining competitive computational efficiency, significantly outperforms existing mainstream algorithms in small object detection on low-quality remote sensing images. Our code is available at this https URL.
Abstract (translated)
在遥感图像中，复杂的背景、弱信号目标和小规模物体使得准确检测变得特别具有挑战性，尤其是在成像质量较低的情况下。一种常见的策略是在检测之前整合单幅图像的超分辨率（SR）处理；然而，这种串联管道通常会面临优化目标不一致、特征冗余以及超分辨率与检测之间缺乏有效交互的问题。为了解决这些问题，我们提出了一种以显著性为导向的多任务协作网络 (SDCoNet)，该网络通过隐式的特征共享将超分辨率和检测结合起来，同时保持各自任务的独特性。SDCoNet 使用基于 Swin 变换器的共享编码器，在此编码器中层次化的窗口偏移自注意力支持跨任务特性合作，并适应性地平衡纹理细化与语义表示之间的权衡。 此外，一个多尺度显著性预测模块生成重要度评分以选择关键令牌，从而能够对弱目标区域集中注意，抑制背景杂乱并压制由多任务结合引入的不利特征。而且，我们还提出了一种梯度路由策略来缓解优化冲突。该策略首先稳定检测语义，并随后将超分辨率梯度沿有利于检测的方向进行路由，使框架引导超分辨率分支生成对检测显式有益的高频细节。 在包括 NWPU VHR-10-Split、DOTAv1.5-Split 和 HRSSD-Split 在内的公共数据集上的实验表明，所提出的方法在低质量遥感图像的小目标检测方面显著优于现有的主流算法，同时保持了具有竞争力的计算效率。我们的代码可在 [这里](https://this https URL) 获取。
URL
https://arxiv.org/abs/2601.12507
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SERA-H: Beyond Native Sentinel Spatial Limits for High-Resolution Canopy Height Mapping
2026-01-15 11:19:00Thomas Boudras, Martin Schwartz, Rasmus Fensholt, Martin Brandt, Ibrahim Fayad, Jean-Pierre Wigneron, Gabriel Belouze, Fajwel Fogel, Philippe CiaisAbstract
High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods using satellite imagery to predict height maps, these approaches often face a trade-off between data accessibility and spatial resolution. To overcome these limitations, we present SERA-H, an end-to-end model combining a super-resolution module (EDSR) and temporal attention encoding (UTAE). Trained under the supervision of high-density LiDAR data (ALS), our model generates 2.5 m resolution height maps from freely available Sentinel-1 and Sentinel-2 (10 m) time series data. Evaluated on an open-source benchmark dataset in France, SERA-H, with a MAE of 2.6 m and a coefficient of determination of 0.82, not only outperforms standard Sentinel-1/2 baselines but also achieves performance comparable to or better than methods relying on commercial very high-resolution imagery (SPOT-6/7, PlanetScope, Maxar). These results demonstrate that combining high-resolution supervision with the spatiotemporal information embedded in time series enables the reconstruction of details beyond the input sensors' native resolution. SERA-H opens the possibility of freely mapping forests with high revisit frequency, achieving accuracy comparable to that of costly commercial imagery.
Abstract (translated)
高分辨率的树冠高度测绘对于森林管理和生物多样性监测至关重要。尽管最近的研究已导致使用卫星图像进行深度学习方法以预测高度图谱的发展，但这些方法通常在数据可访问性和空间分辨率之间存在权衡。为了解决这些问题，我们提出了SERA-H模型，这是一种结合了超分辨率模块（EDSR）和时间注意力编码（UTAE）的端到端模型。我们的模型在高密度激光雷达数据（ALS）的监督下训练，并能从免费提供的Sentinel-1和Sentinel-2（10米）时序数据生成2.5米分辨率的高度图。 在一个法国公开源代码基准数据集上进行评估，SERA-H模型具有2.6米的均方根误差(MAE) 和 0.82 的决定系数，在性能上不仅超越了标准Sentinel-1/2基线方法，也达到了与依赖于商业高分辨率图像（SPOT-6/7、PlanetScope和Maxar）的方法相当或更好的水平。这些结果表明，结合高分辨率监督以及时序数据中嵌入的时空信息可以重建超出输入传感器原生分辨率的细节。 SERA-H模型开启了以低成本实现高频重访的自由森林测绘的可能性，并且其准确性可与昂贵的商业图像相媲美。
URL
https://arxiv.org/abs/2512.18128
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VibrantSR: Sub-Meter Canopy Height Models from Sentinel-2 Using Generative Flow Matching
2026-01-14 20:56:35Kiarie Ndegwa, Andreas Gros, Tony Chang, David Diaz, Vincent A. Landau, Nathan E. Rutenbeck, Luke J. Zachmann, Guy Bayes, Scott ConwayAbstract
We present VibrantSR (Vibrant Super-Resolution), a generative super-resolution framework for estimating 0.5 meter canopy height models (CHMs) from 10 meter Sentinel-2 imagery. Unlike approaches based on aerial imagery that are constrained by infrequent and irregular acquisition schedules, VibrantSR leverages globally available Sentinel-2 seasonal composites, enabling consistent monitoring at a seasonal-to-annual cadence. Evaluated across 22 EPA Level 3 eco-regions in the western United States using spatially disjoint validation splits, VibrantSR achieves a Mean Absolute Error of 4.39 meters for canopy heights >= 2 m, outperforming Meta (4.83 m), LANDFIRE (5.96 m), and ETH (7.05 m) satellite-based benchmarks. While aerial-based VibrantVS (2.71 m MAE) retains an accuracy advantage, VibrantSR enables operational forest monitoring and carbon accounting at continental scales without reliance on costly and temporally infrequent aerial acquisitions.
Abstract (translated)
我们提出了VibrantSR（Vibrant超分辨率），这是一个用于从10米Sentinel-2影像估算0.5米冠层高度模型（CHM）的生成性超分辨率框架。与基于航空影像的方法受限于不频繁且不规律的采集时间表不同，VibrantSR利用了全球可用的Sentinel-2季节合成数据，从而能够在季度至年度的时间尺度上进行一致监测。在使用美国环保署（EPA）Level 3生态区域中22个空间独立验证集的情况下，VibrantSR对于冠层高度≥2米的情况实现了4.39米的平均绝对误差，优于基于卫星的Meta（4.83米）、LANDFIRE（5.96米）和ETH（7.05米）基准。虽然基于航空影像的VibrantVS仍保持着较低误差的优势（2.71米MAE），但VibrantSR能够实现在大陆尺度上进行森林监测和碳排放核算，而不依赖于成本高昂且时间间隔长的航空采集。
URL
https://arxiv.org/abs/2601.09866
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NanoSD: Edge Efficient Foundation Model for Real Time Image Restoration
2026-01-14 19:30:53Subhajit Sanyal, Srinivas Soumitri Miriyala, Akshay Janardan Bankar, Sravanth Kodavanti, Harshit, Abhishek Ameta, Shreyas Pandith, Amit Satish UndeAbstract
Latent diffusion models such as Stable Diffusion 1.5 offer strong generative priors that are highly valuable for image restoration, yet their full pipelines remain too computationally heavy for deployment on edge devices. Existing lightweight variants predominantly compress the denoising U-Net or reduce the diffusion trajectory, which disrupts the underlying latent manifold and limits generalization beyond a single task. We introduce NanoSD, a family of Pareto-optimal diffusion foundation models distilled from Stable Diffusion 1.5 through network surgery, feature-wise generative distillation, and structured architectural scaling jointly applied to the U-Net and the VAE encoder-decoder. This full-pipeline co-design preserves the generative prior while producing models that occupy distinct operating points along the accuracy-latency-size frontier (e.g., 130M-315M parameters, achieving real-time inference down to 20ms on mobile-class NPUs). We show that parameter reduction alone does not correlate with hardware efficiency, and we provide an analysis revealing how architectural balance, feature routing, and latent-space preservation jointly shape true on-device latency. When used as a drop-in backbone, NanoSD enables state-of-the-art performance across image super-resolution, image deblurring, face restoration, and monocular depth estimation, outperforming prior lightweight diffusion models in both perceptual quality and practical deployability. NanoSD establishes a general-purpose diffusion foundation model family suitable for real-time visual generation and restoration on edge devices.
Abstract (translated)
潜扩散模型（如Stable Diffusion 1.5）提供强大的生成先验，这对图像恢复非常有价值。然而，它们的完整管道对于在边缘设备上部署来说计算负担过重。现有的轻量级变体主要通过压缩去噪U-Net或减少扩散轨迹来实现，这会破坏潜在流形并限制单一任务之外的一般化能力。我们引入了NanoSD，这是一个由Stable Diffusion 1.5通过网络手术、特征生成蒸馏和结构化架构扩展共同应用于U-Net和VAE编码器解码器的Pareto最优扩散基础模型家族。这种全管道协同设计在保持生成先验的同时，产生了位于精度延迟大小前沿的不同操作点上的模型（例如，1.3亿至3.15亿参数，在移动类NPUs上实现低至20ms的实时推理）。我们展示了仅减少参数数量与硬件效率之间不存在相关性，并提供了分析揭示架构平衡、特征路由和潜在空间保留如何共同塑造真正的设备延迟。作为现成骨干使用时，NanoSD在图像超分辨率、图像去模糊、面部恢复以及单目深度估计等任务中实现了最先进的性能，在感知质量和实际部署能力上均超过了先前的轻量级扩散模型。NanoSD为实时视觉生成和边缘设备上的图像恢复奠定了通用型扩散基础模型家族的基础。
URL
https://arxiv.org/abs/2601.09823
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Detail Loss in Super-Resolution Models Based on the Laplacian Pyramid and Repeated Upscaling and Downscaling Process
Abstract
With advances in artificial intelligence, image processing has gained significant interest. Image super-resolution is a vital technology closely related to real-world applications, as it enhances the quality of existing images. Since enhancing fine details is crucial for the super-resolution task, pixels that contribute to high-frequency information should be emphasized. This paper proposes two methods to enhance high-frequency details in super-resolution images: a Laplacian pyramid-based detail loss and a repeated upscaling and downscaling process. Total loss with our detail loss guides a model by separately generating and controlling super-resolution and detail images. This approach allows the model to focus more effectively on high-frequency components, resulting in improved super-resolution images. Additionally, repeated upscaling and downscaling amplify the effectiveness of the detail loss by extracting diverse information from multiple low-resolution features. We conduct two types of experiments. First, we design a CNN-based model incorporating our methods. This model achieves state-of-the-art results, surpassing all currently available CNN-based and even some attention-based models. Second, we apply our methods to existing attention-based models on a small scale. In all our experiments, attention-based models adding our detail loss show improvements compared to the originals. These results demonstrate our approaches effectively enhance super-resolution images across different model structures.
Abstract (translated)
随着人工智能的发展，图像处理领域引起了越来越多的关注。超分辨率技术作为其中的重要分支，在提升现有图像质量方面发挥了关键作用。由于增强细节对于超分辨率任务至关重要，因此需要强调那些有助于提供高频信息的像素。本文提出两种方法来增强超分辨率图像中的高频细节：一种是基于拉普拉斯金字塔的细节损失（Laplacian pyramid-based detail loss），另一种则是重复放缩过程（repeated upscaling and downscaling process）。总损失函数结合了我们的细节损失，通过分别生成和控制超分辨率图像和细节图像来引导模型。这种方法使模型能够更有效地聚焦于高频成分，从而提高超分辨率图像的质量。此外，重复放大缩小过程则可以通过从多个低分辨率特征中提取多样信息来增强细节损失的效果。 我们进行了两类实验。首先，设计了一种基于卷积神经网络（CNN）的模型，并将其与我们的方法相结合。该模型取得了最先进的成果，在现有的所有 CNN 基础上甚至某些注意力机制基础的模型性能之上。其次，我们将这些方法小规模地应用到现有的注意力机制基础上的模型中。在我们所有的实验中，加入我们细节损失后的基于注意力机制的模型相比于原始版本均有所改进。这些结果表明我们的方法能够在不同的模型结构下有效提升超分辨率图像的质量。
URL
https://arxiv.org/abs/2601.09410
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S3-CLIP: Video Super Resolution for Person-ReID
2026-01-13 18:46:37Tamas Endrei, Gyorgy CsereyAbstract
Tracklet quality is often treated as an afterthought in most person re-identification (ReID) methods, with the majority of research presenting architectural modifications to foundational models. Such approaches neglect an important limitation, posing challenges when deploying ReID systems in real-world, difficult scenarios. In this paper, we introduce S3-CLIP, a video super-resolution-based CLIP-ReID framework developed for the VReID-XFD challenge at WACV 2026. The proposed method integrates recent advances in super-resolution networks with task-driven super-resolution pipelines, adapting them to the video-based person re-identification setting. To the best of our knowledge, this work represents the first systematic investigation of video super-resolution as a means of enhancing tracklet quality for person ReID, particularly under challenging cross-view conditions. Experimental results demonstrate performance competitive with the baseline, achieving 37.52% mAP in aerial-to-ground and 29.16% mAP in ground-to-aerial scenarios. In the ground-to-aerial setting, S3-CLIP achieves substantial gains in ranking accuracy, improving Rank-1, Rank-5, and Rank-10 performance by 11.24%, 13.48%, and 17.98%, respectively.
Abstract (translated)
在大多数人物重新识别（ReID）方法中，轨迹质量通常被当作次要问题处理，而多数研究集中在对基础模型进行架构上的改进。这种做法忽视了一个重要的局限性，在实际部署ReID系统时会遇到挑战，尤其是在复杂且困难的场景下。在这篇论文中，我们介绍了S3-CLIP框架，这是一个基于视频超分辨率（Video Super-Resolution）和CLIP-ReID技术相结合的方法，并用于WACV 2026年举办的VReID-XFD竞赛。该方法将最近在超分辨率网络中的进展与任务驱动的超分辨率流程集成起来，适应于基于视频的人物重新识别场景。 据我们所知，这项工作是首次系统性地研究视频超分辨率技术如何提升轨迹质量以增强人物ReID性能的研究，特别是在跨视角（cross-view）条件下。实验结果表明，我们的方法在性能上与基准线相当，并且在从空中到地面和从地面到空中的场景中分别达到了37.52%的mAP和29.16%的mAP。尤其是在地面到空中设置的情况下，S3-CLIP框架显著提高了排名准确度，在Rank-1、Rank-5和Rank-10性能方面分别提升了11.24%，13.48%，以及17.98%。
URL
https://arxiv.org/abs/2601.08807
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From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution
2026-01-13 09:01:20Chunyu Meng, Wei Long, Shuhang GuAbstract
Single Image Super-Resolution (SISR) is a fundamental computer vision task that aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input. Transformer-based methods have achieved remarkable performance by modeling long-range dependencies in degraded images. However, their feature-intensive attention computation incurs high computational cost. To improve efficiency, most existing approaches partition images into fixed groups and restrict attention within each group. Such group-wise attention overlooks the inherent asymmetry in token similarities, thereby failing to enable flexible and token-adaptive attention computation. To address this limitation, we propose the Individualized Exploratory Transformer (IET), which introduces a novel Individualized Exploratory Attention (IEA) mechanism that allows each token to adaptively select its own content-aware and independent attention candidates. This token-adaptive and asymmetric design enables more precise information aggregation while maintaining computational efficiency. Extensive experiments on standard SR benchmarks demonstrate that IET achieves state-of-the-art performance under comparable computational complexity.
Abstract (translated)
单图像超分辨率（SISR）是计算机视觉中的一个基本任务，旨在从低分辨率（LR）输入中重建高分辨率（HR）图像。基于Transformer的方法通过建模退化图像中的长距离依赖性而实现了显著的性能提升。然而，它们密集的注意力计算导致了较高的计算成本。为了提高效率，现有的大多数方法将图像划分为固定的组，并限制每个组内的注意机制。这种分组式的注意力忽视了令牌相似性的内在不对称性，因而无法实现灵活且适应于各个令牌的注意力计算。 为了解决这一局限性，我们提出了个性化探索Transformer（IET），它引入了一种新的个性化探索注意力（IEA）机制，允许每个令牌根据自身情况自适应地选择其内容感知且独立的注意候选者。这种针对各令牌进行调整和不对称的设计，在保持计算效率的同时实现了更精确的信息聚合。 在标准超分辨率基准上的大量实验表明，在相似的计算复杂度下，IET达到了最先进的性能水平。
URL
https://arxiv.org/abs/2601.08341
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