Image_Enhancement
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Navigating in the Dark: A Multimodal Framework and Dataset for Nighttime Traffic Sign Recognition
2025-11-21 12:04:53Aditya Mishra, Akshay Agarwal, Haroon LoneAbstract
Traffic signboards are vital for road safety and intelligent transportation systems, enabling navigation and autonomous driving. Yet, recognizing traffic signs at night remains challenging due to visual noise and scarcity of public nighttime datasets. Despite advances in vision architectures, existing methods struggle with robustness under low illumination and fail to leverage complementary mutlimodal cues effectively. To overcome these limitations, firstly, we introduce INTSD, a large-scale dataset comprising street-level night-time images of traffic signboards collected across diverse regions of India. The dataset spans 41 traffic signboard classes captured under varying lighting and weather conditions, providing a comprehensive benchmark for both detection and classification tasks. To benchmark INTSD for night-time sign recognition, we conduct extensive evaluations using state-of-the-art detection and classification models. Secondly, we propose LENS-Net, which integrates an adaptive image enhancement detector for joint illumination correction and sign localization, followed by a structured multimodal CLIP-GCNN classifier that leverages cross-modal attention and graph-based reasoning for robust and semantically consistent recognition. Our method surpasses existing frameworks, with ablation studies confirming the effectiveness of its key components. The dataset and code for LENS-Net is publicly available for research.
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URL
https://arxiv.org/abs/2511.17183
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WWE-UIE: A Wavelet & White Balance Efficient Network for Underwater Image Enhancement
2025-11-20 12:54:08Ching-Heng Cheng, Jen-Wei Lee, Chia-Ming Lee, Chih-Chung HsuAbstract
Underwater Image Enhancement (UIE) aims to restore visibility and correct color distortions caused by wavelength-dependent absorption and scattering. Recent hybrid approaches, which couple domain priors with modern deep neural architectures, have achieved strong performance but incur high computational cost, limiting their practicality in real-time scenarios. In this work, we propose WWE-UIE, a compact and efficient enhancement network that integrates three interpretable priors. First, adaptive white balance alleviates the strong wavelength-dependent color attenuation, particularly the dominance of blue-green tones. Second, a wavelet-based enhancement block (WEB) performs multi-band decomposition, enabling the network to capture both global structures and fine textures, which are critical for underwater restoration. Third, a gradient-aware module (SGFB) leverages Sobel operators with learnable gating to explicitly preserve edge structures degraded by scattering. Extensive experiments on benchmark datasets demonstrate that WWE-UIE achieves competitive restoration quality with substantially fewer parameters and FLOPs, enabling real-time inference on resource-limited platforms. Ablation studies and visualizations further validate the contribution of each component. The source code is available at this https URL.
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URL
https://arxiv.org/abs/2511.16321
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Evaluating Low-Light Image Enhancement Across Multiple Intensity Levels
2025-11-19 14:52:51Maria Pilligua, David Serrano-Lozano, Pai Peng, Ramon Baldrich, Michael S. Brown, Javier Vazquez-CorralAbstract
Imaging in low-light environments is challenging due to reduced scene radiance, which leads to elevated sensor noise and reduced color saturation. Most learning-based low-light enhancement methods rely on paired training data captured under a single low-light condition and a well-lit reference. The lack of radiance diversity limits our understanding of how enhancement techniques perform across varying illumination intensities. We introduce the Multi-Illumination Low-Light (MILL) dataset, containing images captured at diverse light intensities under controlled conditions with fixed camera settings and precise illuminance measurements. MILL enables comprehensive evaluation of enhancement algorithms across variable lighting conditions. We benchmark several state-of-the-art methods and reveal significant performance variations across intensity levels. Leveraging the unique multi-illumination structure of our dataset, we propose improvements that enhance robustness across diverse illumination scenarios. Our modifications achieve up to 10 dB PSNR improvement for DSLR and 2 dB for the smartphone on Full HD images.
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URL
https://arxiv.org/abs/2511.15496
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A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement
2025-11-18 14:20:17Yufeng Tian, Yifan Chen, Zhe Sun, Libang Chen, Mingyu Dou, Jijun Lu, Ye Zheng, Xuelong LiAbstract
Underwater image restoration and enhancement are crucial for correcting color distortion and restoring image details, thereby establishing a fundamental basis for subsequent underwater visual tasks. However, current deep learning methodologies in this area are frequently constrained by the scarcity of high-quality paired datasets. Since it is difficult to obtain pristine reference labels in underwater scenes, existing benchmarks often rely on manually selected results from enhancement algorithms, providing debatable reference images that lack globally consistent color and authentic supervision. This limits the model's capabilities in color restoration, image enhancement, and generalization. To overcome this limitation, we propose using in-air natural images as unambiguous reference targets and translating them into underwater-degraded versions, thereby constructing synthetic datasets that provide authentic supervision signals for model learning. Specifically, we establish a generative data framework based on unpaired image-to-image translation, producing a large-scale dataset that covers 6 representative underwater degradation types. The framework constructs synthetic datasets with precise ground-truth labels, which facilitate the learning of an accurate mapping from degraded underwater images to their pristine scene appearances. Extensive quantitative and qualitative experiments across 6 representative network architectures and 3 independent test sets show that models trained on our synthetic data achieve comparable or superior color restoration and generalization performance to those trained on existing benchmarks. This research provides a reliable and scalable data-driven solution for underwater image restoration and enhancement. The generated dataset is publicly available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2511.14521
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Unified Low-Light Traffic Image Enhancement via Multi-Stage Illumination Recovery and Adaptive Noise Suppression
2025-11-18 10:13:52Siddiqua NamrahAbstract
Enhancing low-light traffic images is crucial for reliable perception in autonomous driving, intelligent transportation, and urban surveillance systems. Nighttime and dimly lit traffic scenes often suffer from poor visibility due to low illumination, noise, motion blur, non-uniform lighting, and glare from vehicle headlights or street lamps, which hinder tasks such as object detection and scene understanding. To address these challenges, we propose a fully unsupervised multi-stage deep learning framework for low-light traffic image enhancement. The model decomposes images into illumination and reflectance components, progressively refined by three specialized modules: (1) Illumination Adaptation, for global and local brightness correction; (2) Reflectance Restoration, for noise suppression and structural detail recovery using spatial-channel attention; and (3) Over-Exposure Compensation, for reconstructing saturated regions and balancing scene luminance. The network is trained using self-supervised reconstruction, reflectance smoothness, perceptual consistency, and domain-aware regularization losses, eliminating the need for paired ground-truth images. Experiments on general and traffic-specific datasets demonstrate superior performance over state-of-the-art methods in both quantitative metrics (PSNR, SSIM, LPIPS, NIQE) and qualitative visual quality. Our approach enhances visibility, preserves structure, and improves downstream perception reliability in real-world low-light traffic scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2511.17612
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ICLR: Inter-Chrominance and Luminance Interaction for Natural Color Restoration in Low-Light Image Enhancement
2025-11-17 17:12:46Xin Xu, Hao Liu, Wei Liu, Wei Wang, Jiayi Wu, Kui JiangAbstract
Low-Light Image Enhancement (LLIE) task aims at improving contrast while restoring details and textures for images captured in low-light conditions. HVI color space has made significant progress in this task by enabling precise decoupling of chrominance and luminance. However, for the interaction of chrominance and luminance branches, substantial distributional differences between the two branches prevalent in natural images limit complementary feature extraction, and luminance errors are propagated to chrominance channels through the nonlinear parameter. Furthermore, for interaction between different chrominance branches, images with large homogeneous-color regions usually exhibit weak correlation between chrominance branches due to concentrated distributions. Traditional pixel-wise losses exploit strong inter-branch correlations for co-optimization, causing gradient conflicts in weakly correlated regions. Therefore, we propose an Inter-Chrominance and Luminance Interaction (ICLR) framework including a Dual-stream Interaction Enhancement Module (DIEM) and a Covariance Correction Loss (CCL). The DIEM improves the extraction of complementary information from two dimensions, fusion and enhancement, respectively. The CCL utilizes luminance residual statistics to penalize chrominance errors and balances gradient conflicts by constraining chrominance branches covariance. Experimental results on multiple datasets show that the proposed ICLR framework outperforms state-of-the-art methods.
Abstract (translated)
URL
https://arxiv.org/abs/2511.13607
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Low-Level Dataset Distillation for Medical Image Enhancement
2025-11-17 08:05:07Fengzhi Xu, Ziyuan Yang, Mengyu Sun, Joey Tianyi Zhou, Yi ZhangAbstract
Medical image enhancement is clinically valuable, but existing methods require large-scale datasets to learn complex pixel-level mappings. However, the substantial training and storage costs associated with these datasets hinder their practical deployment. While dataset distillation (DD) can alleviate these burdens, existing methods mainly target high-level tasks, where multiple samples share the same label. This many-to-one mapping allows distilled data to capture shared semantics and achieve information compression. In contrast, low-level tasks involve a many-to-many mapping that requires pixel-level fidelity, making low-level DD an underdetermined problem, as a small distilled dataset cannot fully constrain the dense pixel-level mappings. To address this, we propose the first low-level DD method for medical image enhancement. We first leverage anatomical similarities across patients to construct the shared anatomical prior based on a representative patient, which serves as the initialization for the distilled data of different patients. This prior is then personalized for each patient using a Structure-Preserving Personalized Generation (SPG) module, which integrates patient-specific anatomical information into the distilled dataset while preserving pixel-level fidelity. For different low-level tasks, the distilled data is used to construct task-specific high- and low-quality training pairs. Patient-specific knowledge is injected into the distilled data by aligning the gradients computed from networks trained on the distilled pairs with those from the corresponding patient's raw data. Notably, downstream users cannot access raw patient data. Instead, only a distilled dataset containing abstract training information is shared, which excludes patient-specific details and thus preserves privacy.
Abstract (translated)
URL
https://arxiv.org/abs/2511.13106
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Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement
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Ultra-low-field (ULF) MRI promises broader accessibility but suffers from low signal-to-noise ratio (SNR), reduced spatial resolution, and contrasts that deviate from high-field standards. Imageto- image translation can map ULF images to a high-field appearance, yet efficacy is limited by scarce paired training data. Working within the ULF-EnC challenge constraints (50 paired 3D volumes; no external data), we study how task-adapted data augmentations impact a standard deep model for ULF image enhancement. We show that strong, diverse augmentations, including auxiliary tasks on high-field data, substantially improve fidelity. Our submission ranked third by brain-masked SSIM on the public validation leaderboard and fourth by the official score on the final test leaderboard. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2511.09366
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IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition
2025-11-04 09:41:33Wenyang Wei, Yang yang, Xixi Jia, Xiangchu Feng, Weiwei Wang, Renzhen WangAbstract
We present IllumFlow, a novel framework that synergizes conditional Rectified Flow (CRF) with Retinex theory for low-light image enhancement (LLIE). Our model addresses low-light enhancement through separate optimization of illumination and reflectance components, effectively handling both lighting variations and noise. Specifically, we first decompose an input image into reflectance and illumination components following Retinex theory. To model the wide dynamic range of illumination variations in low-light images, we propose a conditional rectified flow framework that represents illumination changes as a continuous flow field. While complex noise primarily resides in the reflectance component, we introduce a denoising network, enhanced by flow-derived data augmentation, to remove reflectance noise and chromatic aberration while preserving color fidelity. IllumFlow enables precise illumination adaptation across lighting conditions while naturally supporting customizable brightness enhancement. Extensive experiments on low-light enhancement and exposure correction demonstrate superior quantitative and qualitative performance over existing methods.
Abstract (translated)
URL
https://arxiv.org/abs/2511.02411
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Luminance-Aware Statistical Quantization: Unsupervised Hierarchical Learning for Illumination Enhancement
2025-11-03 12:24:52Derong Kong, Zhixiong Yang, Shengxi Li, Shuaifeng Zhi, Li Liu, Zhen Liu, Jingyuan XiaAbstract
Low-light image enhancement (LLIE) faces persistent challenges in balancing reconstruction fidelity with cross-scenario generalization. While existing methods predominantly focus on deterministic pixel-level mappings between paired low/normal-light images, they often neglect the continuous physical process of luminance transitions in real-world environments, leading to performance drop when normal-light references are unavailable. Inspired by empirical analysis of natural luminance dynamics revealing power-law distributed intensity transitions, this paper introduces Luminance-Aware Statistical Quantification (LASQ), a novel framework that reformulates LLIE as a statistical sampling process over hierarchical luminance distributions. Our LASQ re-conceptualizes luminance transition as a power-law distribution in intensity coordinate space that can be approximated by stratified power functions, therefore, replacing deterministic mappings with probabilistic sampling over continuous luminance layers. A diffusion forward process is designed to autonomously discover optimal transition paths between luminance layers, achieving unsupervised distribution emulation without normal-light references. In this way, it considerably improves the performance in practical situations, enabling more adaptable and versatile light restoration. This framework is also readily applicable to cases with normal-light references, where it achieves superior performance on domain-specific datasets alongside better generalization-ability across non-reference datasets.
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URL
https://arxiv.org/abs/2511.01510
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Versatile and Efficient Medical Image Super-Resolution Via Frequency-Gated Mamba
2025-10-31 09:12:12Wenfeng Huang, Xiangyun Liao, Wei Cao, Wenjing Jia, Weixin SiAbstract
Medical image super-resolution (SR) is essential for enhancing diagnostic accuracy while reducing acquisition cost and scanning time. However, modeling both long-range anatomical structures and fine-grained frequency details with low computational overhead remains challenging. We propose FGMamba, a novel frequency-aware gated state-space model that unifies global dependency modeling and fine-detail enhancement into a lightweight architecture. Our method introduces two key innovations: a Gated Attention-enhanced State-Space Module (GASM) that integrates efficient state-space modeling with dual-branch spatial and channel attention, and a Pyramid Frequency Fusion Module (PFFM) that captures high-frequency details across multiple resolutions via FFT-guided fusion. Extensive evaluations across five medical imaging modalities (Ultrasound, OCT, MRI, CT, and Endoscopic) demonstrate that FGMamba achieves superior PSNR/SSIM while maintaining a compact parameter footprint ($<$0.75M), outperforming CNN-based and Transformer-based SOTAs. Our results validate the effectiveness of frequency-aware state-space modeling for scalable and accurate medical image enhancement.
Abstract (translated)
URL
https://arxiv.org/abs/2510.27296
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Larger Hausdorff Dimension in Scanning Pattern Facilitates Mamba-Based Methods in Low-Light Image Enhancement
2025-10-29 22:25:48Xinhua Wang, Caibo Feng, Xiangjun Fu, Chunxiao LiuAbstract
We propose an innovative enhancement to the Mamba framework by increasing the Hausdorff dimension of its scanning pattern through a novel Hilbert Selective Scan mechanism. This mechanism explores the feature space more effectively, capturing intricate fine-scale details and improving overall coverage. As a result, it mitigates information inconsistencies while refining spatial locality to better capture subtle local interactions without sacrificing the model's ability to handle long-range dependencies. Extensive experiments on publicly available benchmarks demonstrate that our approach significantly improves both the quantitative metrics and qualitative visual fidelity of existing Mamba-based low-light image enhancement methods, all while reducing computational resource consumption and shortening inference time. We believe that this refined strategy not only advances the state-of-the-art in low-light image enhancement but also holds promise for broader applications in fields that leverage Mamba-based techniques.
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URL
https://arxiv.org/abs/2510.26001
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Enhancing rice leaf images: An overview of image denoising techniques
2025-10-28 08:50:17Rupjyoti Chutia, Dibya Jyoti BoraAbstract
Digital image processing involves the systematic handling of images using advanced computer algorithms, and has gained significant attention in both academic and practical fields. Image enhancement is a crucial preprocessing stage in the image-processing chain, improving image quality and emphasizing features. This makes subsequent tasks (segmentation, feature extraction, classification) more reliable. Image enhancement is essential for rice leaf analysis, aiding in disease detection, nutrient deficiency evaluation, and growth analysis. Denoising followed by contrast enhancement are the primary steps. Image filters, generally employed for denoising, transform or enhance visual characteristics like brightness, contrast, and sharpness, playing a crucial role in improving overall image quality and enabling the extraction of useful information. This work provides an extensive comparative study of well-known image-denoising methods combined with CLAHE (Contrast Limited Adaptive Histogram Equalization) for efficient denoising of rice leaf images. The experiments were performed on a rice leaf image dataset to ensure the data is relevant and representative. Results were examined using various metrics to comprehensively test enhancement methods. This approach provides a strong basis for assessing the effectiveness of methodologies in digital image processing and reveals insights useful for future adaptation in agricultural research and other domains.
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URL
https://arxiv.org/abs/2511.00046
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Frequency-Spatial Interaction Driven Network for Low-Light Image Enhancement
2025-10-25 04:17:50Yunhong Tao, Wenbing Tao, Xiang XiangAbstract
Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. With the advent of deep learning, the LLIE technique has achieved significant breakthroughs. However, existing LLIE methods either ignore the important role of frequency domain information or fail to effectively promote the propagation and flow of information, limiting the LLIE performance. In this paper, we develop a novel frequency-spatial interaction-driven network (FSIDNet) for LLIE based on two-stage architecture. To be specific, the first stage is designed to restore the amplitude of low-light images to improve the lightness, and the second stage devotes to restore phase information to refine fine-grained structures. Considering that Frequency domain and spatial domain information are complementary and both favorable for LLIE, we further develop two frequency-spatial interaction blocks which mutually amalgamate the complementary spatial and frequency information to enhance the capability of the model. In addition, we construct the Information Exchange Module (IEM) to associate two stages by adequately incorporating cross-stage and cross-scale features to effectively promote the propagation and flow of information in the two-stage network structure. Finally, we conduct experiments on several widely used benchmark datasets (i.e., LOL-Real, LSRW-Huawei, etc.), which demonstrate that our method achieves the excellent performance in terms of visual results and quantitative metrics while preserving good model efficiency.
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URL
https://arxiv.org/abs/2510.22154
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Restore Text First, Enhance Image Later: Two-Stage Scene Text Image Super-Resolution with Glyph Structure Guidance
2025-10-24 15:59:04Minxing Luo, Linlong Fan, Wang Qiushi, Ge Wu, Yiyan Luo, Yuhang Yu, Jinwei Chen, Yaxing Wang, Qingnan Fan, Jian YangAbstract
Current generative super-resolution methods show strong performance on natural images but distort text, creating a fundamental trade-off between image quality and textual readability. To address this, we introduce \textbf{TIGER} (\textbf{T}ext-\textbf{I}mage \textbf{G}uided sup\textbf{E}r-\textbf{R}esolution), a novel two-stage framework that breaks this trade-off through a \textit{"text-first, image-later"} paradigm. \textbf{TIGER} explicitly decouples glyph restoration from image enhancement: it first reconstructs precise text structures and then uses them to guide subsequent full-image super-resolution. This glyph-to-image guidance ensures both high fidelity and visual consistency. To support comprehensive training and evaluation, we also contribute the \textbf{UltraZoom-ST} (UltraZoom-Scene Text), the first scene text dataset with extreme zoom (\textbf{$\times$14.29}). Extensive experiments show that \textbf{TIGER} achieves \textbf{state-of-the-art} performance, enhancing readability while preserving overall image quality.
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URL
https://arxiv.org/abs/2510.21590
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HistRetinex: Optimizing Retinex model in Histogram Domain for Efficient Low-Light Image Enhancement
2025-10-24 02:24:13Jingtian Zhao, Xueli Xie, Jianxiang Xi, Xiaogang Yang, Haoxuan SunAbstract
Retinex-based low-light image enhancement methods are widely used due to their excellent performance. However, most of them are time-consuming for large-sized images. This paper extends the Retinex model from the spatial domain to the histogram domain, and proposes a novel histogram-based Retinex model for fast low-light image enhancement, named HistRetinex. Firstly, we define the histogram location matrix and the histogram count matrix, which establish the relationship among histograms of the illumination, reflectance and the low-light image. Secondly, based on the prior information and the histogram-based Retinex model, we construct a novel two-level optimization model. Through solving the optimization model, we give the iterative formulas of the illumination histogram and the reflectance histogram, respectively. Finally, we enhance the low-light image through matching its histogram with the one provided by HistRetinex. Experimental results demonstrate that the HistRetinex outperforms existing enhancement methods in both visibility and performance metrics, while executing 1.86 seconds on 1000*664 resolution images, achieving a minimum time saving of 6.67 seconds.
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URL
https://arxiv.org/abs/2510.21100
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BioDet: Boosting Industrial Object Detection with Image Preprocessing Strategies
2025-10-23 21:15:16Jiaqi Hu, Hongli Xu, Junwen Huang, Peter KT Yu, Slobodan Ilic, Benjamin BusamAbstract
Accurate 6D pose estimation is essential for robotic manipulation in industrial environments. Existing pipelines typically rely on off-the-shelf object detectors followed by cropping and pose refinement, but their performance degrades under challenging conditions such as clutter, poor lighting, and complex backgrounds, making detection the critical bottleneck. In this work, we introduce a standardized and plug-in pipeline for 2D detection of unseen objects in industrial settings. Based on current SOTA baselines, our approach reduces domain shift and background artifacts through low-light image enhancement and background removal guided by open-vocabulary detection with foundation models. This design suppresses the false positives prevalent in raw SAM outputs, yielding more reliable detections for downstream pose estimation. Extensive experiments on real-world industrial bin-picking benchmarks from BOP demonstrate that our method significantly boosts detection accuracy while incurring negligible inference overhead, showing the effectiveness and practicality of the proposed method.
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URL
https://arxiv.org/abs/2510.21000
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From Cheap to Pro: A Learning-based Adaptive Camera Parameter Network for Professional-Style Imaging
2025-10-23 13:35:17Fuchen Li, Yansong Du, Wenbo Cheng, Xiaoxia Zhou, Sen YinAbstract
Consumer-grade camera systems often struggle to maintain stable image quality under complex illumination conditions such as low light, high dynamic range, and backlighting, as well as spatial color temperature variation. These issues lead to underexposure, color casts, and tonal inconsistency, which degrade the performance of downstream vision tasks. To address this, we propose ACamera-Net, a lightweight and scene-adaptive camera parameter adjustment network that directly predicts optimal exposure and white balance from RAW inputs. The framework consists of two modules: ACamera-Exposure, which estimates ISO to alleviate underexposure and contrast loss, and ACamera-Color, which predicts correlated color temperature and gain factors for improved color consistency. Optimized for real-time inference on edge devices, ACamera-Net can be seamlessly integrated into imaging pipelines. Trained on diverse real-world data with annotated references, the model generalizes well across lighting conditions. Extensive experiments demonstrate that ACamera-Net consistently enhances image quality and stabilizes perception outputs, outperforming conventional auto modes and lightweight baselines without relying on additional image enhancement modules.
Abstract (translated)
消费者级相机系统在复杂光照条件下（如低光、高动态范围和逆光）以及空间色温变化的情况下，往往难以维持稳定的图像质量。这些问题导致了曝光不足、色调偏差和色彩不一致，从而降低了下游视觉任务的表现。为了解决这一问题，我们提出了ACamera-Net，这是一种轻量级且场景适应性强的相机参数调整网络，可以直接从RAW输入预测最佳的曝光度和白平衡设置。该框架由两个模块组成：ACamera-Exposure用于估计ISO值以减轻曝光不足和对比度损失；ACamera-Color则预测相关色温及增益因子，从而提升色彩一致性。针对边缘设备上的实时推理进行了优化，ACamera-Net可以无缝集成到成像流水线中。通过使用带有注释参考的多样化真实世界数据进行训练，该模型在各种光照条件下表现良好且具有泛化能力。广泛的实验表明，与传统自动模式和轻量级基线相比，ACamera-Net在无需额外图像增强模块的情况下持续改善了图像质量和稳定了感知输出，并表现出色。
URL
https://arxiv.org/abs/2510.20550
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Boosting Fidelity for Pre-Trained-Diffusion-Based Low-Light Image Enhancement via Condition Refinement
2025-10-20 02:40:06Xiaogang Xu, Jian Wang, Yunfan Lu, Ruihang Chu, Ruixing Wang, Jiafei Wu, Bei Yu, Liang LinAbstract
Diffusion-based methods, leveraging pre-trained large models like Stable Diffusion via ControlNet, have achieved remarkable performance in several low-level vision tasks. However, Pre-Trained Diffusion-Based (PTDB) methods often sacrifice content fidelity to attain higher perceptual realism. This issue is exacerbated in low-light scenarios, where severely degraded information caused by the darkness limits effective control. We identify two primary causes of fidelity loss: the absence of suitable conditional latent modeling and the lack of bidirectional interaction between the conditional latent and noisy latent in the diffusion process. To address this, we propose a novel optimization strategy for conditioning in pre-trained diffusion models, enhancing fidelity while preserving realism and aesthetics. Our method introduces a mechanism to recover spatial details lost during VAE encoding, i.e., a latent refinement pipeline incorporating generative priors. Additionally, the refined latent condition interacts dynamically with the noisy latent, leading to improved restoration performance. Our approach is plug-and-play, seamlessly integrating into existing diffusion networks to provide more effective control. Extensive experiments demonstrate significant fidelity improvements in PTDB methods.
Abstract (translated)
基于扩散的方法，通过利用如ControlNet中的预训练大型模型Stable Diffusion，在若干低级视觉任务中取得了显著的性能。然而，预训练的扩散基方法（PTDB）常常为了达到更高的感知真实感而牺牲内容保真度。这一问题在低光场景中尤为严重，由于黑暗导致的信息严重退化限制了有效控制。 我们确定了两个主要的内容损失原因：缺乏合适的条件潜在模型以及条件潜在与噪声潜在之间缺乏双向交互作用。为解决这些问题，我们提出了一种新的预训练扩散模型的条件优化策略，在保持真实感和美学的同时提高保真度。我们的方法引入了一个机制来恢复VAE编码过程中丢失的空间细节，即一个包含生成先验的潜在精炼管道。此外，经过精细处理后的潜在条件与噪声潜在进行动态交互，从而提升了修复性能。 本方法采用插件式的集成方式，可以无缝地整合到现有的扩散网络中以提供更有效的控制能力。广泛的实验表明，在预训练扩散模型方法中实现了显著的内容保真度提升。
URL
https://arxiv.org/abs/2510.17105
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Rethinking Efficient Hierarchical Mixing Architecture for Low-light RAW Image Enhancement
2025-10-17 10:09:38Xianmin Chen, Peiliang Huang, Longfei Han, Dingwen Zhang, Junwei HanAbstract
Low-light RAW image enhancement remains a challenging task. Although numerous deep learning based approaches have been proposed, they still suffer from inherent limitations. A key challenge is how to simultaneously achieve strong enhancement quality and high efficiency. In this paper, we rethink the architecture for efficient low-light image signal processing (ISP) and introduce a Hierarchical Mixing Architecture (HiMA). HiMA leverages the complementary strengths of Transformer and Mamba modules to handle features at large and small scales, respectively, thereby improving efficiency while avoiding the ambiguities observed in prior two-stage frameworks. To further address uneven illumination with strong local variations, we propose Local Distribution Adjustment (LoDA), which adaptively aligns feature distributions across different local regions. In addition, to fully exploit the denoised outputs from the first stage, we design a Multi-prior Fusion (MPF) module that integrates spatial and frequency-domain priors for detail enhancement. Extensive experiments on multiple public datasets demonstrate that our method outperforms state-of-the-art approaches, achieving superior performance with fewer parameters. Code will be released at this https URL.
Abstract (translated)
低光RAW图像增强仍然是一个具有挑战性的任务。尽管已经提出了许多基于深度学习的方法，但它们仍然存在固有的局限性。主要的挑战是如何同时实现强大的增强质量和高效率。在本文中，我们重新思考了高效的低光图像信号处理（ISP）架构，并引入了一种分层混合架构（HiMA）。HiMA利用Transformer和Mamba模块的优势来分别处理大尺度和小尺度特征，从而提高效率并避免先前两阶段框架中存在的模糊性。为了解决强烈的局部变化引起的光照不均问题，我们提出了局部分布调整（LoDA），它能够自适应地对不同局部区域的特性分布进行校准。此外，为了充分利用第一阶段去噪后的输出，我们设计了一个多先验融合（MPF）模块，该模块集成了空间和频域先验以增强细节。在多个公共数据集上的广泛实验表明，我们的方法超越了现有的最先进技术，在参数更少的情况下实现了更好的性能。代码将在[提供链接]发布。
URL
https://arxiv.org/abs/2510.15497
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