Image_Enhancement
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LoR-LUT: Learning Compact 3D Lookup Tables via Low-Rank Residuals
2026-02-26 04:28:35Ziqi Zhao, Abhijit Mishra, Shounak RoychowdhuryAbstract
We present LoR-LUT, a unified low-rank formulation for compact and interpretable 3D lookup table (LUT) generation. Unlike conventional 3D-LUT-based techniques that rely on fusion of basis LUTs, which are usually dense tensors, our unified approach extends the current framework by jointly using residual corrections, which are in fact low-rank tensors, together with a set of basis LUTs. The approach described here improves the existing perceptual quality of an image, which is primarily due to the technique's novel use of residual corrections. At the same time, we achieve the same level of trilinear interpolation complexity, using a significantly smaller number of network, residual corrections, and LUT parameters. The experimental results obtained from LoR-LUT, which is trained on the MIT-Adobe FiveK dataset, reproduce expert-level retouching characteristics with high perceptual fidelity and a sub-megabyte model size. Furthermore, we introduce an interactive visualization tool, termed LoR-LUT Viewer, which transforms an input image into the LUT-adjusted output image, via a number of slidebars that control different parameters. The tool provides an effective way to enhance interpretability and user confidence in the visual results. Overall, our proposed formulation offers a compact, interpretable, and efficient direction for future LUT-based image enhancement and style transfer.
Abstract (translated)
我们提出了LoR-LUT，这是一种用于生成紧凑且可解释的3D查找表（LUT）的统一低秩表示方法。与传统的基于3D-LUT的方法不同，后者依赖于基LUT的融合，而这些基LUT通常是密集张量，我们的统一方法通过同时使用残差校正来扩展当前框架，这些残差实际上是低秩张量，并与一组基LUT结合使用。这里描述的方法主要由于其对残差校正的新颖应用，提高了图像现有的感知质量。与此同时，我们利用了显著较少的网络、残差校正和LUT参数，在保持相同的三线性插值复杂度的情况下实现了这一目标。 在MIT-Adobe FiveK数据集上训练得到的LoR-LUT模型能够以不到1MB的大小高度逼真地复现专家级图像修饰特性，具有良好的感知保真度。此外，我们还引入了一个交互式可视化工具，名为LoR-LUT Viewer，通过调整多个滑块来控制不同参数，该工具可以将输入图片转换为经过LUT校准后的输出图片。此工具提供了一种有效的方法以增强视觉结果的可解释性和用户信心。 总体而言，我们的方法提供了未来基于LUT的图像增强和风格迁移的一种紧凑、可解释且高效的途径。
URL
https://arxiv.org/abs/2602.22607
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Adaptive Illumination Control for Robot Perception
2026-02-13 03:34:01Yash Turkar, Shekoufeh Sadeghi, Karthik DantuAbstract
Robot perception under low light or high dynamic range is usually improved downstream - via more robust feature extraction, image enhancement, or closed-loop exposure control. However, all of these approaches are limited by the image captured these conditions. An alternate approach is to utilize a programmable onboard light that adds to ambient illumination and improves captured images. However, it is not straightforward to predict its impact on image formation. Illumination interacts nonlinearly with depth, surface reflectance, and scene geometry. It can both reveal structure and induce failure modes such as specular highlights and saturation. We introduce Lightning, a closed-loop illumination-control framework for visual SLAM that combines relighting, offline optimization, and imitation learning. This is performed in three stages. First, we train a Co-Located Illumination Decomposition (CLID) relighting model that decomposes a robot observation into an ambient component and a light-contribution field. CLID enables physically consistent synthesis of the same scene under alternative light intensities and thereby creates dense multi-intensity training data without requiring us to repeatedly re-run trajectories. Second, using these synthesized candidates, we formulate an offline Optimal Intensity Schedule (OIS) problem that selects illumination levels over a sequence trading off SLAM-relevant image utility against power consumption and temporal smoothness. Third, we distill this ideal solution into a real-time controller through behavior cloning, producing an Illumination Control Policy (ILC) that generalizes beyond the initial training distribution and runs online on a mobile robot to command discrete light-intensity levels. Across our evaluation, Lightning substantially improves SLAM trajectory robustness while reducing unnecessary illumination power.
Abstract (translated)
URL
https://arxiv.org/abs/2602.15900
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Enhancing Underwater Images via Adaptive Semantic-aware Codebook Learning
2026-02-11 07:20:15Bosen Lin, Feng Gao, Yanwei Yu, Junyu Dong, Qian DuAbstract
Underwater Image Enhancement (UIE) is an ill-posed problem where natural clean references are not available, and the degradation levels vary significantly across semantic regions. Existing UIE methods treat images with a single global model and ignore the inconsistent degradation of different scene components. This oversight leads to significant color distortions and loss of fine details in heterogeneous underwater scenes, especially where degradation varies significantly across different image regions. Therefore, we propose SUCode (Semantic-aware Underwater Codebook Network), which achieves adaptive UIE from semantic-aware discrete codebook representation. Compared with one-shot codebook-based methods, SUCode exploits semantic-aware, pixel-level codebook representation tailored to heterogeneous underwater degradation. A three-stage training paradigm is employed to represent raw underwater image features to avoid pseudo ground-truth contamination. Gated Channel Attention Module (GCAM) and Frequency-Aware Feature Fusion (FAFF) jointly integrate channel and frequency cues for faithful color restoration and texture recovery. Extensive experiments on multiple benchmarks demonstrate that SUCode achieves state-of-the-art performance, outperforming recent UIE methods on both reference and no-reference metrics. The code will be made public available at this https URL.
Abstract (translated)
水下图像增强（UIE）是一个不适定问题，因为自然干净的参考图无法获取，并且退化程度在语义区域间变化显著。现有的UIE方法采用单一全局模型处理图像，忽略了不同场景组件之间不一致的退化情况。这种忽视会导致在异构水下场景中出现明显的色彩扭曲和细节丢失，尤其是在图像的不同区域退化差异较大的情况下更为严重。因此，我们提出了SUCode（语义感知水下载入网络），它利用语义感知离散载入表示实现自适应UIE。与一次生成的载入方法不同，SUCode采用针对异构水下退化的像素级语义载入表示。 为了防止伪真实标签污染，在训练过程中采用了三阶段训练范式以表征原始水下图像特征。门控通道注意模块（GCAM）和频率感知特性融合（FAFF）共同集成了信道和频率线索，用于忠实的色彩恢复和纹理重建。在多个基准测试中的广泛实验表明，SUCode取得了最先进的性能，在参考和无参考指标上均优于最近的UIE方法。 代码将在以下网址公开发布：[此链接] (请根据实际情况提供具体URL)
URL
https://arxiv.org/abs/2602.10586
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Thermal odometry and dense mapping using learned ddometry and Gaussian splatting
2026-02-07 11:07:44Tianhao Zhou, Yujia Chen, Zhihao Zhan, Yuhang Ming, Jianzhu HuaiAbstract
Thermal infrared sensors, with wavelengths longer than smoke particles, can capture imagery independent of darkness, dust, and smoke. This robustness has made them increasingly valuable for motion estimation and environmental perception in robotics, particularly in adverse conditions. Existing thermal odometry and mapping approaches, however, are predominantly geometric and often fail across diverse datasets while lacking the ability to produce dense maps. Motivated by the efficiency and high-quality reconstruction ability of recent Gaussian Splatting (GS) techniques, we propose TOM-GS, a thermal odometry and mapping method that integrates learning-based odometry with GS-based dense mapping. TOM-GS is among the first GS-based SLAM systems tailored for thermal cameras, featuring dedicated thermal image enhancement and monocular depth integration. Extensive experiments on motion estimation and novel-view rendering demonstrate that TOM-GS outperforms existing learning-based methods, confirming the benefits of learning-based pipelines for robust thermal odometry and dense reconstruction.
Abstract (translated)
热红外传感器，其波长比烟雾颗粒长，能够捕捉不受黑暗、灰尘和烟雾影响的图像。这种鲁棒性使它们在机器人运动估计和环境感知中变得越来越有价值，尤其是在恶劣条件下。然而，现有的热里程计和映射方法主要是几何学上的，并且经常在不同的数据集中失效，同时缺乏生成密集地图的能力。受最近高斯点阵（GS）技术的效率和高质量重建能力启发，我们提出了TOM-GS，这是一种将基于学习的里程计与基于GS的密集映射集成在一起的热里程计和映射方法。TOM-GS是首个专为热相机设计并采用GS为基础的SLAM系统的之一，它具有专门针对热图像增强及单目深度整合的功能。在运动估计和新视角渲染方面的广泛实验表明，TOM-GS优于现有的基于学习的方法，确认了基于学习的工作流程对于稳健的热里程计和密集重建的好处。
URL
https://arxiv.org/abs/2602.07493
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U-Net Based Image Enhancement for Short-time Muon Scattering Tomography
2026-02-05 10:11:15Haochen Wang, Pei Yu, Liangwen Chen, Weibo He, Yu Zhang, Yuhong Yu, Xueheng Zhang, Lei Yang, Zhiyu SunAbstract
Muon Scattering Tomography (MST) is a promising non-invasive inspection technique, yet the practical application of short-time MST is hindered by poor image quality due to limited muon flux. To address this limitation, we propose a U-Net-based framework trained on Point of Closest Approach (PoCA) images reconstructed with simulation MST data to enhance image quality. When applied to experimental MST data, the framework significantly improves image quality, increasing the Structural Similarity Index Measure (SSIM) from 0.7232 to 0.9699 and decreasing the Learned Perceptual Image Patch Similarity (LPIPS) from 0.3604 to 0.0270. These results demonstrate that our method can effectively enhance low-statistics MST images, thereby paving the way for the practical deployment of short-time MST.
Abstract (translated)
缪子散射断层成像（MST）是一种有前景的非侵入性检测技术，然而由于缪子通量有限导致图像质量不佳，短时间内的实际应用受到了限制。为了解决这一局限，我们提出了一种基于U-Net框架的方法，该方法使用模拟MST数据重建的最接近点成像（PoCA）进行训练，以提升图像质量。当应用于实验性的MST数据时，此框架显著改善了图像的质量，将结构相似性指数测量（SSIM）从0.7232提高到0.9699，并且将学习感知图像块相似度（LPIPS）从0.3604降低至0.0270。这些结果表明我们的方法能够有效提升低统计量的MST图像的质量，从而为短时间MST的实际应用铺平了道路。
URL
https://arxiv.org/abs/2602.07060
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LOBSTgER-enhance: an underwater image enhancement pipeline
2026-02-05 00:29:04Andreas Mentzelopoulos, Keith EllenbogenAbstract
Underwater photography presents significant inherent challenges including reduced contrast, spatial blur, and wavelength-dependent color distortions. These effects can obscure the vibrancy of marine life and awareness photographers in particular are often challenged with heavy post-processing pipelines to correct for these distortions. We develop an image-to-image pipeline that learns to reverse underwater degradations by introducing a synthetic corruption pipeline and learning to reverse its effects with diffusion-based generation. Training and evaluation are performed on a small high-quality dataset of awareness photography images by Keith Ellenbogen. The proposed methodology achieves high perceptual consistency and strong generalization in synthesizing 512x768 images using a model of ~11M parameters after training from scratch on ~2.5k images.
Abstract (translated)
水下摄影面临诸多固有挑战，包括对比度降低、空间模糊以及依赖波长的颜色失真。这些效果会遮蔽海洋生物的鲜艳色彩，并且摄影师常常需要面对繁重的后期处理流程来纠正这些问题。我们开发了一种图像到图像的流水线，通过引入合成腐蚀流水线并学习用基于扩散的方法逆转其影响，从而学会消除水下退化现象。训练和评估是在Keith Ellenbogen的一组高质量意识摄影的小型数据集上进行的。所提出的方法在使用约1100万个参数模型从头开始训练大约2500张图像后，在合成分辨率为512x768像素的图像时，达到了高度感知一致性和强大的泛化能力。
URL
https://arxiv.org/abs/2602.05163
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Development of Domain-Invariant Visual Enhancement and Restoration Approach for Underwater Images
2026-01-30 11:58:40Rajini Makam, Sharanya Patil, Dhatri Shankari T M, Suresh Sundaram, Narasimhan SundararajanAbstract
Underwater images suffer severe degradation due to wavelength-dependent attenuation, scattering, and illumination non-uniformity that vary across water types and depths. We propose an unsupervised Domain-Invariant Visual Enhancement and Restoration (DIVER) framework that integrates empirical correction with physics-guided modeling for robust underwater image enhancement. DIVER first applies either IlluminateNet for adaptive luminance enhancement or a Spectral Equalization Filter for spectral normalization. An Adaptive Optical Correction Module then refines hue and contrast using channel-adaptive filtering, while Hydro-OpticNet employs physics-constrained learning to compensate for backscatter and wavelength-dependent attenuation. The parameters of IlluminateNet and Hydro-OpticNet are optimized via unsupervised learning using a composite loss function. DIVER is evaluated on eight diverse datasets covering shallow, deep, and highly turbid environments, including both naturally low-light and artificially illuminated scenes, using reference and non-reference metrics. While state-of-the-art methods such as WaterNet, UDNet, and Phaseformer perform reasonably in shallow water, their performance degrades in deep, unevenly illuminated, or artificially lit conditions. In contrast, DIVER consistently achieves best or near-best performance across all datasets, demonstrating strong domain-invariant capability. DIVER yields at least a 9% improvement over SOTA methods in UCIQE. On the low-light SeaThru dataset, where color-palette references enable direct evaluation of color restoration, DIVER achieves at least a 4.9% reduction in GPMAE compared to existing methods. Beyond visual quality, DIVER also improves robotic perception by enhancing ORB-based keypoint repeatability and matching performance, confirming its robustness across diverse underwater environments.
Abstract (translated)
URL
https://arxiv.org/abs/2601.22878
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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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