Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer
Images captured in poorly lit conditions are often corrupted by acquisition noise. Leveraging recent advances in graph-based regularization, we propose a fast Retinex-based restoration scheme that denoises and contrast-enhances an image. Specifically, by Retinex theory we first assume that each image pixel is a multiplication of its reflectance and illumination components. We next assume that the reflectance and illumination components are piecewise constant (PWC) and continuous piecewise planar (PWP) signals, which can be recovered via graph Laplacian regularizer (GLR) and gradient graph Laplacian regularizer (GGLR) respectively. We formulate quadratic objectives regularized by GLR and GGLR, which are minimized alternately until convergence by solving linear systems -- with improved condition numbers via proposed preconditioners -- via conjugate gradient (CG) efficiently. Experimental results show that our algorithm achieves competitive visual image quality while reducing computation complexity noticeably.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingImage DenoisingSimilar Papers 제목 키워드 기반
Self-supervised Low Light Image Enhancement and Denoising
This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising. The…
DenoisingImage EnhancementLow-Light Image EnhancementRetinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement
Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. Many deep learning-based methods have been developed to address thi…
DemosaickingDenoisingExposure CorrectionImage Enhancement+2Bio-Inspired Night Image Enhancement Based on Contrast Enhancement and Denoising
Due to the low accuracy of object detection and recognition in many intelligent surveillance systems at nighttime, the quality of night images is crucial. Compared with the corresponding daytime image, nighttime image is…
DenoisingImage Enhancementobject-detectionObject DetectionDeep Retinex Decomposition for Low-Light Enhancement
Retinex model is an effective tool for low-light image enhancement. It assumes that observed images can be decomposed into the reflectance and illumination. Most existing Retinex-based methods have carefully designed han…
DenoisingImage EnhancementLow-Light Image EnhancementA Retinex-based Image Enhancement Scheme with Noise Aware Shadow-up Function
This paper proposes a novel image contrast enhancement method based on both a noise aware shadow-up function and Retinex (retina and cortex) decomposition. Under low light conditions, images taken by digital cameras have…
Image Enhancement