Paper Reading AI Learner

RetinexDual: Retinex-based Dual Nature Approach for Generalized Ultra-High-Definition Image Restoration

2025-08-06 18:15:05
Mohab Kishawy, Ali Abdellatif Hussein, Jun Chen

Abstract

Advancements in image sensing have elevated the importance of Ultra-High-Definition Image Restoration (UHD IR). Traditional methods, such as extreme downsampling or transformation from the spatial to the frequency domain, encounter significant drawbacks: downsampling induces irreversible information loss in UHD images, while our frequency analysis reveals that pure frequency-domain approaches are ineffective for spatially confined image artifacts, primarily due to the loss of degradation locality. To overcome these limitations, we present RetinexDual, a novel Retinex theory-based framework designed for generalized UHD IR tasks. RetinexDual leverages two complementary sub-networks: the Scale-Attentive maMBA (SAMBA) and the Frequency Illumination Adaptor (FIA). SAMBA, responsible for correcting the reflectance component, utilizes a coarse-to-fine mechanism to overcome the causal modeling of mamba, which effectively reduces artifacts and restores intricate details. On the other hand, FIA ensures precise correction of color and illumination distortions by operating in the frequency domain and leveraging the global context provided by it. Evaluating RetinexDual on four UHD IR tasks, namely deraining, deblurring, dehazing, and Low-Light Image Enhancement (LLIE), shows that it outperforms recent methods qualitatively and quantitatively. Ablation studies demonstrate the importance of employing distinct designs for each branch in RetinexDual, as well as the effectiveness of its various components.

Abstract (translated)

在图像传感技术的进步推动下,超高清图像恢复(UHD IR)的重要性日益增加。传统方法,如极端降采样或从空间域转换到频率域的方法,在实际应用中遇到了显著的局限性:降采样会导致超高清图像中的不可逆信息损失;而我们的频谱分析表明,纯粹基于频率域的方法对于处理局部化退化的图像缺陷无效,主要是因为它们丢失了损坏的具体位置信息。为了解决这些问题,我们提出了RetinexDual,这是一种新型框架,基于Retinex理论设计用于通用的UHD IR任务。 RetinexDual采用两个互补的子网络:尺度注意多级双边滤波器(Scale-Attentive maMBA, SAMBA)和频率光照适配器(Frequency Illumination Adaptor, FIA)。SAMBA负责校正反射分量,通过从粗到细的工作机制克服了mamba模型的因果限制,从而有效地减少了伪影并恢复细节。另一方面,FIA在频域中操作,并利用全局上下文来确保准确地纠正颜色和光照失真。 通过对雨线移除、模糊消除、雾霾去除以及低光图像增强(LLIE)这四个UHD IR任务进行评估,RetinexDual在定性和定量分析上都优于最近的方法。消融研究展示了在RetinexDual中为每个分支采用不同设计的重要性,并证明了其各个组件的有效性。

URL

https://arxiv.org/abs/2508.04797

PDF

https://arxiv.org/pdf/2508.04797.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot