Image_Enhancement
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Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling
2026-01-29 15:28:25Shujaat Khan, Syed Muhammad Atif, Jaeyoung Huh, Syed Saad AzharAbstract
Ultrasound (US) interpretation is hampered by multiplicative speckle, acquisition blur from the point-spread function (PSF), and scanner- and operator-dependent artifacts. Supervised enhancement methods assume access to clean targets or known degradations; conditions rarely met in practice. We present a blind, self-supervised enhancement framework that jointly deconvolves and denoises B-mode images using a Swin Convolutional U-Net trained with a \emph{physics-guided} degradation model. From each training frame, we extract rotated/cropped patches and synthesize inputs by (i) convolving with a Gaussian PSF surrogate and (ii) injecting noise via either spatial additive Gaussian noise or complex Fourier-domain perturbations that emulate phase/magnitude distortions. For US scans, clean-like targets are obtained via non-local low-rank (NLLR) denoising, removing the need for ground truth; for natural images, the originals serve as targets. Trained and validated on UDIAT~B, JNU-IFM, and XPIE Set-P, and evaluated additionally on a 700-image PSFHS test set, the method achieves the highest PSNR/SSIM across Gaussian and speckle noise levels, with margins that widen under stronger corruption. Relative to MSANN, Restormer, and DnCNN, it typically preserves an extra $\sim$1--4\,dB PSNR and 0.05--0.15 SSIM in heavy Gaussian noise, and $\sim$2--5\,dB PSNR and 0.05--0.20 SSIM under severe speckle. Controlled PSF studies show reduced FWHM and higher peak gradients, evidence of resolution recovery without edge erosion. Used as a plug-and-play preprocessor, it consistently boosts Dice for fetal head and pubic symphysis segmentation. Overall, the approach offers a practical, assumption-light path to robust US enhancement that generalizes across datasets, scanners, and degradation types.
Abstract (translated)
URL
https://arxiv.org/abs/2601.21856
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Real-Time Human Activity Recognition on Edge Microcontrollers: Dynamic Hierarchical Inference with Multi-Spectral Sensor Fusion
2026-01-29 15:21:45Boyu Li, Kuangji Zuo, Lincong Li, Yonghui WuAbstract
The demand for accurate on-device pattern recognition in edge applications is intensifying, yet existing approaches struggle to reconcile accuracy with computational constraints. To address this challenge, a resource-aware hierarchical network based on multi-spectral fusion and interpretable modules, namely the Hierarchical Parallel Pseudo-image Enhancement Fusion Network (HPPI-Net), is proposed for real-time, on-device Human Activity Recognition (HAR). Deployed on an ARM Cortex-M4 microcontroller for low-power real-time inference, HPPI-Net achieves 96.70% accuracy while utilizing only 22.3 KiB of RAM and 439.5 KiB of ROM after optimization. HPPI-Net employs a two-layer architecture. The first layer extracts preliminary features using Fast Fourier Transform (FFT) spectrograms, while the second layer selectively activates either a dedicated module for stationary activity recognition or a parallel LSTM-MobileNet network (PLMN) for dynamic states. PLMN fuses FFT, Wavelet, and Gabor spectrograms through three parallel LSTM encoders and refines the concatenated features using Efficient Channel Attention (ECA) and Depthwise Separable Convolution (DSC), thereby offering channel-level interpretability while substantially reducing multiply-accumulate operations. Compared with MobileNetV3, HPPI-Net improves accuracy by 1.22% and reduces RAM usage by 71.2% and ROM usage by 42.1%. These results demonstrate that HPPI-Net achieves a favorable accuracy-efficiency trade-off and provides explainable predictions, establishing a practical solution for wearable, industrial, and smart home HAR on memory-constrained edge platforms.
Abstract (translated)
URL
https://arxiv.org/abs/2602.00152
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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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Revisiting Lightweight Low-Light Image Enhancement: From a YUV Color Space Perspective
2026-01-24 07:27:54Hailong Yan, Shice Liu, Xiangtao Zhang, Lujian Yao, Fengxiang Yang, Jinwei Chen, Bo LiAbstract
In the current era of mobile internet, Lightweight Low-Light Image Enhancement (L3IE) is critical for mobile devices, which faces a persistent trade-off between visual quality and model compactness. While recent methods employ disentangling strategies to simplify lightweight architectural design, such as Retinex theory and YUV color space transformations, their performance is fundamentally limited by overlooking channel-specific degradation patterns and cross-channel interactions. To address this gap, we perform a frequency-domain analysis that confirms the superiority of the YUV color space for L3IE. We identify a key insight: the Y channel primarily loses low-frequency content, while the UV channels are corrupted by high-frequency noise. Leveraging this finding, we propose a novel YUV-based paradigm that strategically restores channels using a Dual-Stream Global-Local Attention module for the Y channel, a Y-guided Local-Aware Frequency Attention module for the UV channels, and a Guided Interaction module for final feature fusion. Extensive experiments validate that our model establishes a new state-of-the-art on multiple benchmarks, delivering superior visual quality with a significantly lower parameter count.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17349
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LL-GaussianMap: Zero-shot Low-Light Image Enhancement via 2D Gaussian Splatting Guided Gain Maps
2026-01-22 08:57:36Yuhan Chen, Ying Fang, Guofa Li, Wenxuan Yu, Yicui Shi, Jingrui Zhang, Kefei Qian, Wenbo Chu, Keqiang LiAbstract
Significant progress has been made in low-light image enhancement with respect to visual quality. However, most existing methods primarily operate in the pixel domain or rely on implicit feature representations. As a result, the intrinsic geometric structural priors of images are often neglected. 2D Gaussian Splatting (2DGS) has emerged as a prominent explicit scene representation technique characterized by superior structural fitting capabilities and high rendering efficiency. Despite these advantages, the utilization of 2DGS in low-level vision tasks remains unexplored. To bridge this gap, LL-GaussianMap is proposed as the first unsupervised framework incorporating 2DGS into low-light image enhancement. Distinct from conventional methodologies, the enhancement task is formulated as a gain map generation process guided by 2DGS primitives. The proposed method comprises two primary stages. First, high-fidelity structural reconstruction is executed utilizing 2DGS. Then, data-driven enhancement dictionary coefficients are rendered via the rasterization mechanism of Gaussian splatting through an innovative unified enhancement module. This design effectively incorporates the structural perception capabilities of 2DGS into gain map generation, thereby preserving edges and suppressing artifacts during enhancement. Additionally, the reliance on paired data is circumvented through unsupervised learning. Experimental results demonstrate that LL-GaussianMap achieves superior enhancement performance with an extremely low storage footprint, highlighting the effectiveness of explicit Gaussian representations for image enhancement.
Abstract (translated)
在低光图像增强方面，视觉质量有了显著的进步。然而，大多数现有的方法主要是在像素领域操作或依赖隐式的特征表示。因此，通常会忽略图像的内在几何结构先验。二维高斯点阵（2DGS）作为一种显式场景表示技术脱颖而出，其特点是具有出色的结构拟合能力和高效的渲染效率。尽管有这些优点，但将2DGS应用于低级视觉任务的研究尚未进行。 为了填补这一空白，提出了LL-GaussianMap——第一个将2DGS集成到低光图像增强中的无监督框架。不同于传统的方法，增强任务被定义为一个由2DGS原语引导的增益图生成过程。所提出的方法包括两个主要阶段：首先利用2DGS执行高保真结构重建；然后通过新颖的统一增强模块和高斯点阵的光栅化机制渲染数据驱动的增强字典系数。这种设计有效地将2DGS的结构感知能力整合到增益图生成中，在图像增强过程中保持边缘并抑制伪影。 此外，该方法通过无监督学习避免了对配对数据的依赖。实验结果表明，LL-GaussianMap在存储占用极低的情况下达到了卓越的增强性能，这突显了明确高斯表示法对于图像增强的有效性。
URL
https://arxiv.org/abs/2601.15766
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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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Harmonizing the Deep: A Unified Information Pipeline for Robust Marine Biodiversity Assessment Across Heterogeneous Domains
2026-01-20 13:51:55Marco Piccolo, Qiwei Han, Astrid van Toor, Joachim VannesteAbstract
Marine biodiversity monitoring requires scalability and reliability across complex underwater environments to support conservation and invasive-species management. Yet existing detection solutions often exhibit a pronounced deployment gap, with performance degrading sharply when transferred to new sites. This work establishes the foundational detection layer for a multi-year invasive species monitoring initiative targeting Arctic and Atlantic marine ecosystems. We address this challenge by developing a Unified Information Pipeline that standardises heterogeneous datasets into a comparable information flow and evaluates a fixed, deployment-relevant detector under controlled cross-domain protocols. Across multiple domains, we find that structural factors, such as scene composition, object density, and contextual redundancy, explain cross-domain performance loss more strongly than visual degradation such as turbidity, with sparse scenes inducing a characteristic "Context Collapse" failure mode. We further validate operational feasibility by benchmarking inference on low-cost edge hardware, showing that runtime optimisation enables practical sampling rates for remote monitoring. The results shift emphasis from image enhancement toward structure-aware reliability, providing a democratised tool for consistent marine ecosystem assessment.
Abstract (translated)
海洋生物多样性监测需要在复杂的水下环境中具备可扩展性和可靠性，以支持保护和入侵物种管理。然而，现有的检测解决方案通常存在明显的部署差距，在转移到新地点时性能会急剧下降。这项工作为一项多年期的入侵物种监控倡议建立了基础检测层，目标是北极和大西洋海洋生态系统。我们通过开发一个统一的信息管道来应对这一挑战，该管道将异构数据集标准化为可比较的信息流，并在受控跨领域协议下评估固定、与部署相关的检测器。我们发现，在多个领域中，结构因素如场景构成、物体密度和上下文冗余比视觉退化（例如浑浊）更强烈地解释了跨域性能的损失，并且稀疏场景会引发一个特定的“上下文崩溃”失败模式。 为了验证操作可行性，我们在低成本边缘硬件上进行了推理基准测试，结果表明运行时优化能够使远程监控的实际采样率变得可行。这些成果将重点从图像增强转移到结构感知可靠性，提供了用于一致评估海洋生态系统的民主化工具。
URL
https://arxiv.org/abs/2601.13975
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SGW-GAN: Sliced Gromov-Wasserstein Guided GANs for Retinal Fundus Image Enhancement
2026-01-19 21:57:18Yujian Xiong, Xuanzhao Dong, Wenhui Zhu, Xin Li, Oana Dumitrascu, Yalin WangAbstract
Retinal fundus photography is indispensable for ophthalmic screening and diagnosis, yet image quality is often degraded by noise, artifacts, and uneven illumination. Recent GAN- and diffusion-based enhancement methods improve perceptual quality by aligning degraded images with high-quality distributions, but our analysis shows that this focus can distort intra-class geometry: clinically related samples become dispersed, disease-class boundaries blur, and downstream tasks such as grading or lesion detection are harmed. The Gromov Wasserstein (GW) discrepancy offers a principled solution by aligning distributions through internal pairwise distances, naturally preserving intra-class structure, but its high computational cost restricts practical use. To overcome this, we propose SGW-GAN, the first framework to incorporate Sliced GW (SGW) into retinal image enhancement. SGW approximates GW via random projections, retaining relational fidelity while greatly reducing cost. Experiments on public datasets show that SGW-GAN produces visually compelling enhancements, achieves superior diabetic retinopathy grading, and reports the lowest GW discrepancy across disease labels, demonstrating both efficiency and clinical fidelity for unpaired medical image enhancement.
Abstract (translated)
视网膜眼底摄影对于眼科筛查和诊断至关重要，然而图像质量常常因噪声、伪影及不均匀照明而受损。近年来基于生成对抗网络（GAN）和扩散模型的增强方法通过将退化图像与高质量分布对齐来提升感知质量，但我们的分析表明这种侧重会扭曲类内几何结构：临床相关的样本变得分散，疾病类别边界模糊，并且下游任务如分级或病变检测受到影响。Gromov-Wasserstein (GW) 差异提供了通过内部成对距离对齐分布的原理性解决方案，自然地保留了类内结构，但由于其高昂的计算成本，在实际应用中受到了限制。 为解决这一问题，我们提出了一种新的框架——SGW-GAN（Sliced Gromov-Wasserstein Generative Adversarial Network），首次将切片Gromov-Wasserstein (SGW) 融入视网膜图像增强。SGW 通过随机投影来近似 GW 差异，在大幅降低成本的同时保留了关系保真度。实验结果表明，SGW-GAN 在公共数据集上生成的视觉改进效果显著，糖尿病性视网膜病变分级表现优越，并且各类疾病标签之间的GW差异最小，从而展示了在未配对医学图像增强中同时具备高效性和临床精准性的能力。
URL
https://arxiv.org/abs/2601.13417
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Mobile-friendly Image de-noising: Hardware Conscious Optimization for Edge Application
2026-01-16 09:39:01Srinivas Miriyala, Sowmya Vajrala, Hitesh Kumar, Sravanth Kodavanti, Vikram RajendiranAbstract
Image enhancement is a critical task in computer vision and photography that is often entangled with noise. This renders the traditional Image Signal Processing (ISP) ineffective compared to the advances in deep learning. However, the success of such methods is increasingly associated with the ease of their deployment on edge devices, such as smartphones. This work presents a novel mobile-friendly network for image de-noising obtained with Entropy-Regularized differentiable Neural Architecture Search (NAS) on a hardware-aware search space for a U-Net architecture, which is first-of-its-kind. The designed model has 12% less parameters, with ~2-fold improvement in ondevice latency and 1.5-fold improvement in the memory footprint for a 0.7% drop in PSNR, when deployed and profiled on Samsung Galaxy S24 Ultra. Compared to the SOTA Swin-Transformer for Image Restoration, the proposed network had competitive accuracy with ~18-fold reduction in GMACs. Further, the network was tested successfully for Gaussian de-noising with 3 intensities on 4 benchmarks and real-world de-noising on 1 benchmark demonstrating its generalization ability.
Abstract (translated)
图像增强是计算机视觉和摄影中的一个关键任务，通常会受到噪声的影响。这使得传统的图像信号处理（ISP）在与深度学习的进展相比时显得不够有效。然而，此类方法的成功越来越依赖于其在边缘设备（如智能手机）上部署的简便性。本工作提出了一种新型的手机友好型网络用于图像去噪，该网络通过基于硬件感知搜索空间上的熵正则化可微神经架构搜索(NAS)获得，这是首次实现这一概念的设计。 设计的模型参数减少了12%，在三星Galaxy S24 Ultra上部署和评估时，设备端延迟提高了约两倍，内存占用减少了一半，而PSNR仅下降了0.7%。与最先进的Swin-Transformer图像恢复方法相比，该提议的网络具有竞争性的准确性，并且GMACs减少了大约18倍。 此外，在四个基准测试上成功对三个强度级别的高斯去噪进行了测试，并在一个真实世界基准测试中验证了其去噪能力，从而证明了该模型具备良好的泛化能力。
URL
https://arxiv.org/abs/2601.11684
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ViewMorpher3D: A 3D-aware Diffusion Framework for Multi-Camera Novel View Synthesis in Autonomous Driving
2026-01-12 13:44:14Farhad G. Zanjani, Hong Cai, Amirhossein HabibianAbstract
Autonomous driving systems rely heavily on multi-view images to ensure accurate perception and robust decision-making. To effectively develop and evaluate perception stacks and planning algorithms, realistic closed-loop simulators are indispensable. While 3D reconstruction techniques such as Gaussian Splatting offer promising avenues for simulator construction, the rendered novel views often exhibit artifacts, particularly in extrapolated perspectives or when available observations are sparse. We introduce ViewMorpher3D, a multi-view image enhancement framework based on image diffusion models, designed to elevate photorealism and multi-view coherence in driving scenes. Unlike single-view approaches, ViewMorpher3D jointly processes a set of rendered views conditioned on camera poses, 3D geometric priors, and temporally adjacent or spatially overlapping reference views. This enables the model to infer missing details, suppress rendering artifacts, and enforce cross-view consistency. Our framework accommodates variable numbers of cameras and flexible reference/target view configurations, making it adaptable to diverse sensor setups. Experiments on real-world driving datasets demonstrate substantial improvements in image quality metrics, effectively reducing artifacts while preserving geometric fidelity.
Abstract (translated)
自动驾驶系统高度依赖多视角图像以确保准确的感知和稳健的决策制定。为了有效地开发和评估感知栈与规划算法，现实主义闭环模拟器不可或缺。虽然像高斯点阵（Gaussian Splatting）这样的三维重建技术为构建模拟器提供了有前景的方法，但渲染的新视角往往会出现伪影，尤其是在外推视图或可用观察数据稀疏的情况下。我们推出了ViewMorpher3D，这是一个基于图像扩散模型的多视角图像增强框架，旨在提升驾驶场景中的照片真实感和多视角一致性。 与单视角方法不同，ViewMorpher3D同时处理一组渲染视角，这些视角以相机姿态、三维几何先验知识以及时间上相邻或空间上重叠的参考视图为条件。这使得模型能够推断缺失细节，抑制渲染伪影，并确保跨视角的一致性。我们的框架支持可变数量的摄像头和灵活的参考/目标视角配置，使其适用于各种传感器设置。 在真实驾驶数据集上的实验表明，在图像质量指标上取得了显著提升，有效减少了伪影同时保持了几何保真度。
URL
https://arxiv.org/abs/2601.07540
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Bridging Robustness and Efficiency: Real-Time Low-Light Enhancement via Attention U-Net GAN
2026-01-10 10:39:22Yash Thesia, Meera SutharAbstract
Recent advancements in Low-Light Image Enhancement (LLIE) have focused heavily on Diffusion Probabilistic Models, which achieve high perceptual quality but suffer from significant computational latency (often exceeding 2-4 seconds per image). Conversely, traditional CNN-based baselines offer real-time inference but struggle with "over-smoothing," failing to recover fine structural details in extreme low-light conditions. This creates a practical gap in the literature: the lack of a model that provides generative-level texture recovery at edge-deployable speeds. In this paper, we address this trade-off by proposing a hybrid Attention U-Net GAN. We demonstrate that the heavy iterative sampling of diffusion models is not strictly necessary for texture recovery. Instead, by integrating Attention Gates into a lightweight U-Net backbone and training within a conditional adversarial framework, we can approximate the high-frequency fidelity of generative models in a single forward pass. Extensive experiments on the SID dataset show that our method achieves a best-in-class LPIPS score of 0.112 among efficient models, significantly outperforming efficient baselines (SID, EnlightenGAN) while maintaining an inference latency of 0.06s. This represents a 40x speedup over latent diffusion models, making our approach suitable for near real-time applications.
Abstract (translated)
最近在低光图像增强（LLIE）领域的进展主要集中在扩散概率模型上，这类模型虽然能够达到很高的感知质量，但计算延迟严重（每张图片处理时间往往超过2-4秒）。相比之下，传统的基于CNN的方法则能提供实时推理，但在极端低光照条件下却难以避免“过度平滑”问题，无法恢复精细的结构细节。这在文献中造成了一个实际差距：缺乏既能生成高质量纹理又能实现边缘部署速度的模型。 在这篇论文中，我们通过提出一种混合注意力U-Net GAN来解决上述权衡问题。我们证明了扩散模型中的密集迭代采样并非进行纹理恢复所必需。相反，通过将注意力门控集成到轻量级U-Net骨干网络，并在条件对抗框架内训练，可以在一次前向传递中近似生成模型的高频保真度。 我们在SID数据集上的广泛实验表明，我们的方法达到了0.112的最佳LPIPS分数（在高效模型类别中），远超其他高效基准（如SID和EnlightenGAN）的表现，并且保持了0.06秒的推理延迟。这代表了对潜在扩散模型40倍的速度提升，使得我们的方法适用于近乎实时的应用场景。
URL
https://arxiv.org/abs/2601.06518
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Integrated Framework for Selecting and Enhancing Ancient Marathi Inscription Images from Stone, Metal Plate, and Paper Documents
2026-01-08 10:30:59Bapu D. Chendage, Rajivkumar S. MenteAbstract
Ancient script images often suffer from severe background noise, low contrast, and degradation caused by aging and environmental effects. In many cases, the foreground text and background exhibit similar visual characteristics, making the inscriptions difficult to read. The primary objective of image enhancement is to improve the readability of such degraded ancient images. This paper presents an image enhancement approach based on binarization and complementary preprocessing techniques for removing stains and enhancing unclear ancient text. The proposed methods are evaluated on different types of ancient scripts, including inscriptions on stone, metal plates, and historical documents. Experimental results show that the proposed approach achieves classification accuracies of 55.7%, 62%, and 65.6% for stone, metal plate, and document scripts, respectively, using the K-Nearest Neighbor (K-NN) classifier. Using the Support Vector Machine (SVM) classifier, accuracies of 53.2%, 59.5%, and 67.8% are obtained. The results demonstrate the effectiveness of the proposed enhancement method in improving the readability of ancient Marathi inscription images.
Abstract (translated)
古代文字图像常常受到严重背景噪声、低对比度以及由于老化和环境因素导致的退化问题的影响。在许多情况下，前景文本与背景具有相似的视觉特征，这使得这些刻文难以阅读。图像增强的主要目标是改善此类受损古文字图像的可读性。 本文提出了一种基于二值化和互补预处理技术的方法，用于去除污渍并增强模糊不清的古代文本。所提出的这种方法在不同类型的历史文献上进行了评估，包括石刻、金属板以及历史文件上的铭文。实验结果表明，在使用K-Nearest Neighbor（K-NN）分类器时，对于石刻、金属板和文档脚本分别获得了55.7%、62% 和 65.6% 的分类准确性；在使用支持向量机（SVM）分类器时，则分别达到了53.2%、59.5% 和 67.8% 的准确率。这些结果证明了所提出增强方法的有效性，特别是在改善古代马拉地语铭文图像的可读性方面。 通过这种方法，可以更有效地对古文字进行数字化和研究，为历史学家提供更加清晰的研究材料。
URL
https://arxiv.org/abs/2601.04800
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Edit2Restore:Few-Shot Image Restoration via Parameter-Efficient Adaptation of Pre-trained Editing Models
2026-01-06 19:56:16M. Ak{\i}n Y{\i}lmaz, Ahmet Bilican, Burak Can Biner, A. Murat TekalpAbstract
Image restoration has traditionally required training specialized models on thousands of paired examples per degradation type. We challenge this paradigm by demonstrating that powerful pre-trained text-conditioned image editing models can be efficiently adapted for multiple restoration tasks through parameter-efficient fine-tuning with remarkably few examples. Our approach fine-tunes LoRA adapters on FLUX.1 Kontext, a state-of-the-art 12B parameter flow matching model for image-to-image translation, using only 16-128 paired images per task, guided by simple text prompts that specify the restoration operation. Unlike existing methods that train specialized restoration networks from scratch with thousands of samples, we leverage the rich visual priors already encoded in large-scale pre-trained editing models, dramatically reducing data requirements while maintaining high perceptual quality. A single unified LoRA adapter, conditioned on task-specific text prompts, effectively handles multiple degradations including denoising, deraining, and dehazing. Through comprehensive ablation studies, we analyze: (i) the impact of training set size on restoration quality, (ii) trade-offs between task-specific versus unified multi-task adapters, (iii) the role of text encoder fine-tuning, and (iv) zero-shot baseline performance. While our method prioritizes perceptual quality over pixel-perfect reconstruction metrics like PSNR/SSIM, our results demonstrate that pre-trained image editing models, when properly adapted, offer a compelling and data-efficient alternative to traditional image restoration approaches, opening new avenues for few-shot, prompt-guided image enhancement. The code to reproduce our results are available at: this https URL
Abstract (translated)
图像恢复传统上需要针对每种退化类型在数千对示例数据上训练专门的模型。我们挑战这一范式，通过展示强大的预训练文本条件图像编辑模型可以仅使用少量配对样本进行高效的参数高效微调（Parameter-Efficient Fine-Tuning），从而适应多种恢复任务。我们的方法是在FLUX.1 Kontext上微调LoRA适配器，FLUX.1 Kontext是用于图到图转换的最先进的120亿参数流匹配模型，每项任务仅使用16至128对图像，并通过简单的文本提示来指导特定的恢复操作。与现有方法不同的是，这些方法从零开始训练专门的恢复网络，需要数千个样本，我们利用大规模预训练编辑模型中已编码的丰富视觉先验知识，在减少数据需求的同时保持高感知质量。 一个单一统一的LoRA适配器，基于任务特定的文本提示，可以有效地处理包括去噪、去雨和去雾在内的多种退化。通过全面的消融研究，我们分析了：（i）训练集大小对恢复质量的影响；（ii）针对特定任务与统一多任务适配器之间的权衡；（iii）文本编码器微调的作用；以及（iv）零样本基线性能。 虽然我们的方法优先考虑感知质量而不是像素完美的重建指标，如PSNR/SSIM，但结果表明，当这些预训练的图像编辑模型得到适当调整时，它们提供了一种有吸引力且数据高效的替代传统图像恢复方法的方法，并为少量样本、提示引导的图像增强开辟了新的途径。 用于重现我们结果的代码可在以下网址获取：[请在此处插入正确的链接]
URL
https://arxiv.org/abs/2601.03391
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Nighttime Hazy Image Enhancement via Progressively and Mutually Reinforcing Night-Haze Priors
2026-01-05 10:58:02Chen Zhu, Huiwen Zhang, Mu He, Yujie Li, Xiaotian QiaoAbstract
Enhancing the visibility of nighttime hazy images is challenging due to the complex degradation distributions. Existing methods mainly address a single type of degradation (e.g., haze or low-light) at a time, ignoring the interplay of different degradation types and resulting in limited visibility improvement. We observe that the domain knowledge shared between low-light and haze priors can be reinforced mutually for better visibility. Based on this key insight, in this paper, we propose a novel framework that enhances visibility in nighttime hazy images by reinforcing the intrinsic consistency between haze and low-light priors mutually and progressively. In particular, our model utilizes image-, patch-, and pixel-level experts that operate across visual and frequency domains to recover global scene structure, regional patterns, and fine-grained details progressively. A frequency-aware router is further introduced to adaptively guide the contribution of each expert, ensuring robust image restoration. Extensive experiments demonstrate the superior performance of our model on nighttime dehazing benchmarks both quantitatively and qualitatively. Moreover, we showcase the generalizability of our model in daytime dehazing and low-light enhancement tasks.
Abstract (translated)
夜间模糊图像的清晰度增强由于复杂的退化分布而具有挑战性。现有的方法主要一次解决一种类型的退化（例如，雾或低光），忽略了不同退化类型之间的相互作用，导致可见性的提升有限。我们观察到，低光和雾先验之间共享的领域知识可以通过相互强化来提高更好的可视性。基于这一关键见解，在本文中，我们提出了一种新的框架，通过在夜间模糊图像上相互且逐步地增强雾和低光先验之间的内在一致性来提升可见度。 具体来说，我们的模型利用了跨视觉和频率域工作的图像级、补丁级和像素级专家，以逐步恢复全局场景结构、区域模式和细粒度细节。此外，我们引入了一个频率感知路由器，以自适应地指导每个专家的贡献，确保稳健的图像修复。广泛的实验表明，在夜间去雾基准测试中，我们的模型在定量和定性上都表现出优越的性能。此外，我们展示了我们的模型在白天去雾和低光增强任务中的泛化能力。
URL
https://arxiv.org/abs/2601.01998
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DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments
2025-12-31 17:31:29Yohan Park, Hyunwoo Ha, Wonjun Jo, Tae-Hyun OhAbstract
Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations, including low-light conditions at night or in dark environments--a core necessity that has been largely overlooked. To address this underexplored challenge, we present DarkEQA, an open-source benchmark for evaluating EQA-relevant perceptual primitives under multi-level low-light conditions. DarkEQA isolates the perception bottleneck by evaluating question answering from egocentric observations under controlled degradations, enabling attributable robustness analysis. A key design feature of DarkEQA is its physical fidelity: visual degradations are modeled in linear RAW space, simulating physics-based illumination drop and sensor noise followed by an ISP-inspired rendering pipeline. We demonstrate the utility of DarkEQA by evaluating a wide range of state-of-the-art VLMs and Low-Light Image Enhancement (LLIE) models. Our analysis systematically reveals VLMs' limitations when operating under these challenging visual conditions. Our code and benchmark dataset will be released upon acceptance.
Abstract (translated)
视觉语言模型（VLM）作为具身代理的核心推理模块被越来越多地采用。现有的基准测试主要评估它们在理想条件下的表现，如光线充足的环境，然而，为了实现全天候可靠运行，在包括夜间或黑暗环境中低光照等广泛视觉退化情况下的性能是必不可少的，这一点却被很大程度上忽视了。为了解决这个未充分探索的挑战，我们提出了DarkEQA，这是一个开源基准测试平台，用于在多级低光条件下评估与问答相关的感觉基本单元的表现。 DarkEQA通过在受控退化的第一人称视角观察中进行问题回答来隔离感知瓶颈，从而允许归因于稳健性的分析。DarkEQA的关键设计特性是物理保真度：视觉退化是在线性RAW空间中建模的，模拟了基于物理学的照明下降和传感器噪声，并通过一个启发自ISP（图像信号处理）的渲染管线进行。 我们通过评估一系列最先进的VLMs和低光图像增强（LLIE）模型来展示了DarkEQA的实用性。我们的分析系统地揭示了视觉语言模型在这些具有挑战性的视觉条件下的局限性。我们的代码和基准测试数据集将在被接受后发布。
URL
https://arxiv.org/abs/2512.24985
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Data relativistic uncertainty framework for low-illumination anime scenery image enhancement
Abstract
By contrast with the prevailing works of low-light enhancement in natural images and videos, this study copes with the low-illumination quality degradation in anime scenery images to bridge the domain gap. For such an underexplored enhancement task, we first curate images from various sources and construct an unpaired anime scenery dataset with diverse environments and illumination conditions to address the data scarcity. To exploit the power of uncertainty information inherent with the diverse illumination conditions, we propose a Data Relativistic Uncertainty (DRU) framework, motivated by the idea from Relativistic GAN. By analogy with the wave-particle duality of light, our framework interpretably defines and quantifies the illumination uncertainty of dark/bright samples, which is leveraged to dynamically adjust the objective functions to recalibrate the model learning under data uncertainty. Extensive experiments demonstrate the effectiveness of DRU framework by training several versions of EnlightenGANs, yielding superior perceptual and aesthetic qualities beyond the state-of-the-art methods that are incapable of learning from data uncertainty perspective. We hope our framework can expose a novel paradigm of data-centric learning for potential visual and language domains. Code is available.
Abstract (translated)
与现有的自然图像和视频低光增强作品相比，本研究针对动漫风景图像中的低光照质量退化问题，旨在弥合领域差异。为了应对这一尚未充分探索的增强任务，我们首先从各种来源收集图片，并构建了一个包含多样化环境和光照条件的无配对动漫风景数据集，以解决数据稀缺的问题。 为了利用与多样化的照明条件固有的不确定性信息的力量，我们提出了一种数据相对论不确定性（DRU）框架，这一框架受到了相对论生成对抗网络思想的启发。通过将光的波粒二象性类比到我们的框架中，我们明确地定义和量化了暗/亮样本的光照不确定性，并利用这种不确定性动态调整目标函数以在数据不确定性的情况下重新校准模型学习。 大量的实验展示了DRU框架的有效性，通过对几个版本的EnlightenGAN进行训练，结果超越了当前最先进的方法，在感知质量和美学质量方面均表现出色。我们希望我们的框架可以揭示一种新的以数据为中心的学习范式，适用于潜在的视觉和语言领域。代码已公开提供。
URL
https://arxiv.org/abs/2512.21944
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JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement
2025-12-23 10:12:35Tao Ye, Hongbin Ren, Chongbing Zhang, Haoran Chen, Xiaosong LiAbstract
Given the complexity of underwater environments and the variability of water as a medium, underwater images are inevitably subject to various types of degradation. The degradations present nonlinear coupling rather than simple superposition, which renders the effective processing of such coupled degradations particularly challenging. Most existing methods focus on designing specific branches, modules, or strategies for specific degradations, with little attention paid to the potential information embedded in their coupling. Consequently, they struggle to effectively capture and process the nonlinear interactions of multiple degradations from a bottom-up perspective. To address this issue, we propose JDPNet, a joint degradation processing network, that mines and unifies the potential information inherent in coupled degradations within a unified framework. Specifically, we introduce a joint feature-mining module, along with a probabilistic bootstrap distribution strategy, to facilitate effective mining and unified adjustment of coupled degradation features. Furthermore, to balance color, clarity, and contrast, we design a novel AquaBalanceLoss to guide the network in learning from multiple coupled degradation losses. Experiments on six publicly available underwater datasets, as well as two new datasets constructed in this study, show that JDPNet exhibits state-of-the-art performance while offering a better tradeoff between performance, parameter size, and computational cost.
Abstract (translated)
鉴于水下环境的复杂性和作为介质的水的变异性，水下图像不可避免地会遭受各种类型的退化。这些退化的表现是非线性的耦合关系，而非简单的叠加效应，这使得有效处理这种耦合退化尤为困难。大多数现有的方法主要集中在为特定退化设计专门的分支、模块或策略上，并且很少关注其耦合中存在的潜在信息。因此，它们难以从下至上有效地捕捉并处理多种退化的非线性交互作用。为了应对这一挑战，我们提出了JDPNet（联合退化处理网络），该网络在一个统一的框架内挖掘和整合了耦合退化中固有的潜在信息。具体而言，我们引入了一个联合特征挖掘模块以及一种概率引导分布策略，以促进对耦合退化特征的有效挖掘与统一分配调整。此外，为了平衡色彩、清晰度和对比度，我们设计了一种新的AquaBalanceLoss来指导网络从多种耦合退化的损失中学习。 我们在六个公开可用的水下数据集上以及在这项研究中构建的两个新数据集上的实验表明，JDPNet展示了最先进的性能，并且在性能、参数大小和计算成本之间提供了更好的权衡。
URL
https://arxiv.org/abs/2512.20213
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MMMamba: A Versatile Cross-Modal In Context Fusion Framework for Pan-Sharpening and Zero-Shot Image Enhancement
2025-12-17 10:07:09Yingying Wang, Xuanhua He, Chen Wu, Jialing Huang, Suiyun Zhang, Rui Liu, Xinghao Ding, Haoxuan CheAbstract
Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by integrating a high-resolution panchromatic (PAN) image with its corresponding low-resolution multispectral (MS) image. To achieve effective fusion, it is crucial to fully exploit the complementary information between the two modalities. Traditional CNN-based methods typically rely on channel-wise concatenation with fixed convolutional operators, which limits their adaptability to diverse spatial and spectral variations. While cross-attention mechanisms enable global interactions, they are computationally inefficient and may dilute fine-grained correspondences, making it difficult to capture complex semantic relationships. Recent advances in the Multimodal Diffusion Transformer (MMDiT) architecture have demonstrated impressive success in image generation and editing tasks. Unlike cross-attention, MMDiT employs in-context conditioning to facilitate more direct and efficient cross-modal information exchange. In this paper, we propose MMMamba, a cross-modal in-context fusion framework for pan-sharpening, with the flexibility to support image super-resolution in a zero-shot manner. Built upon the Mamba architecture, our design ensures linear computational complexity while maintaining strong cross-modal interaction capacity. Furthermore, we introduce a novel multimodal interleaved (MI) scanning mechanism that facilitates effective information exchange between the PAN and MS modalities. Extensive experiments demonstrate the superior performance of our method compared to existing state-of-the-art (SOTA) techniques across multiple tasks and benchmarks.
Abstract (translated)
光谱锐化（Pan-sharpening）的目标是通过整合高分辨率全色（PAN）图像和对应的低分辨率多光谱（MS）图像，生成高质量的高分辨多光谱（HRMS）图像。为了实现有效的融合，充分挖掘这两种模态之间的互补信息至关重要。传统的基于CNN的方法通常依赖于通道级串联以及固定的卷积操作符，这限制了它们适应各种空间和光谱变化的能力。虽然交叉注意力机制能够提供全局交互作用，但其计算效率较低，并且可能稀释精细对应关系，难以捕捉复杂的语义关系。 最近，在多模态扩散变压器（MMDiT）架构上的进展在图像生成和编辑任务中取得了令人印象深刻的成功表现。不同于交叉注意力机制，MMDiT采用上下文条件化以促进更加直接高效的跨模态信息交换。本文提出了一种新的光谱锐化框架——MMMamba，这是一种基于Mamba架构的跨模态上下文融合框架，并且能够支持零样本图像超分辨率任务。 我们的设计在确保线性计算复杂度的同时，仍然保持了强大的跨模态交互能力。此外，我们还引入了一个新颖的多模态交错（MI）扫描机制，以促进PAN和MS模态之间的有效信息交换。广泛的实验结果证明了我们的方法在多个任务和基准上的性能优于现有的最先进（SOTA）技术。
URL
https://arxiv.org/abs/2512.15261
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ERIENet: An Efficient RAW Image Enhancement Network under Low-Light Environment
2025-12-17 08:36:23Jianan Wang, Yang Hong, Hesong Li, Tao Wang, Songrong Liu, Ying FuAbstract
RAW images have shown superior performance than sRGB images in many image processing tasks, especially for low-light image enhancement. However, most existing methods for RAW-based low-light enhancement usually sequentially process multi-scale information, which makes it difficult to achieve lightweight models and high processing speeds. Besides, they usually ignore the green channel superiority of RAW images, and fail to achieve better reconstruction performance with good use of green channel information. In this work, we propose an efficient RAW Image Enhancement Network (ERIENet), which parallelly processes multi-scale information with efficient convolution modules, and takes advantage of rich information in green channels to guide the reconstruction of images. Firstly, we introduce an efficient multi-scale fully-parallel architecture with a novel channel-aware residual dense block to extract feature maps, which reduces computational costs and achieves real-time processing speed. Secondly, we introduce a green channel guidance branch to exploit the rich information within the green channels of the input RAW image. It increases the quality of reconstruction results with few parameters and computations. Experiments on commonly used low-light image enhancement datasets show that ERIENet outperforms state-of-the-art methods in enhancing low-light RAW images with higher effiency. It also achieves an optimal speed of over 146 frame-per-second (FPS) for 4K-resolution images on a single NVIDIA GeForce RTX 3090 with 24G memory.
Abstract (translated)
许多图像处理任务中，RAW格式的图片相较于sRGB格式表现出更优的性能，尤其是在低光环境下的图片增强。然而，大多数现有的基于RAW数据进行低光增强的方法通常会按顺序处理多尺度信息，这使得构建轻量级模型和实现高速处理变得困难。此外，这些方法往往忽视了RAW图像绿色通道的优势，并未能充分利用绿色通道的信息来实现更好的重建性能。 为此，在这项工作中，我们提出了一种高效的RAW图像增强网络（ERIENet），该网络采用高效卷积模块并行地处理多尺度信息，并利用丰富的绿色通道信息指导图片的重建。具体来说： 1. 我们引入了一个高效的全并行多尺度架构和一个新颖的感知通道残差密集块，以提取特征图，这可以减少计算成本，实现实时处理速度。 2. 我们还引入了一条绿色通道引导分支，用于挖掘输入RAW图像中绿色通道内的丰富信息。这一做法在使用很少的参数和计算的情况下提升了重建结果的质量。 实验结果显示，在常用的低光图片增强数据集上，ERIENet相较于现有的最先进方法，在提升低光RAW图片质量方面表现出了更高的效率，并且对于4K分辨率的图像能够在单个NVIDIA GeForce RTX 3090（24G内存）显卡上以超过146帧每秒的速度运行。
URL
https://arxiv.org/abs/2512.15186
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MFE-GAN: Efficient GAN-based Framework for Document Image Enhancement and Binarization with Multi-scale Feature Extraction
2025-12-16 05:54:27Rui-Yang Ju, KokSheik Wong, Yanlin Jin, Jen-Shiun ChiangAbstract
Document image enhancement and binarization are commonly performed prior to document analysis and recognition tasks for improving the efficiency and accuracy of optical character recognition (OCR) systems. This is because directly recognizing text in degraded documents, particularly in color images, often results in unsatisfactory recognition performance. To address these issues, existing methods train independent generative adversarial networks (GANs) for different color channels to remove shadows and noise, which, in turn, facilitates efficient text information extraction. However, deploying multiple GANs results in long training and inference times. To reduce both training and inference times of document image enhancement and binarization models, we propose MFE-GAN, an efficient GAN-based framework with multi-scale feature extraction (MFE), which incorporates Haar wavelet transformation (HWT) and normalization to process document images before feeding them into GANs for training. In addition, we present novel generators, discriminators, and loss functions to improve the model's performance, and we conduct ablation studies to demonstrate their effectiveness. Experimental results on the Benchmark, Nabuco, and CMATERdb datasets demonstrate that the proposed MFE-GAN significantly reduces the total training and inference times while maintaining comparable performance with respect to state-of-the-art (SOTA) methods. The implementation of this work is available at this https URL.
Abstract (translated)
文档图像增强和二值化通常在文档分析和识别任务之前进行，以提高光学字符识别（OCR）系统的效率和准确性。这是因为直接从质量较差的文档中（尤其是在彩色图像中）识别文本往往会导致不理想的识别性能。为了解决这些问题，现有的方法训练独立的生成对抗网络（GANs），针对不同的颜色通道来移除阴影和噪声，这有助于高效地提取文本信息。然而，部署多个GAN会增加训练和推理的时间。 为了减少文档图像增强和二值化模型的训练及推理时间，我们提出了一种基于GAN的高效框架MFE-GAN，该框架集成了多尺度特征提取（MFE），使用Haar小波变换（HWT）和标准化处理文档图像，在输入给GAN进行训练之前对其进行预处理。此外，我们还提出了新颖的生成器、判别器和损失函数以提高模型性能，并通过消融研究证明了它们的有效性。 在Benchmark、Nabuco和CMATERdb数据集上的实验结果表明，所提出的MFE-GAN显著减少了总的训练和推理时间，同时保持与最先进的（SOTA）方法相当的性能。这项工作的实现可在以下网址获得：[此处应插入实际链接]。
URL
https://arxiv.org/abs/2512.14114
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