Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer

TL;DR

This paper introduces a fast Retinex-based image restoration method that denoises and enhances contrast by leveraging graph Laplacian regularizers to model reflectance and illumination components, achieving high-quality results efficiently.

Contribution

It proposes a novel Retinex-based restoration scheme using graph Laplacian regularizers for denoising and contrast enhancement, with improved computational efficiency.

Findings

Achieves competitive visual quality in image denoising and contrast enhancement.

Reduces computational complexity compared to existing methods.

Uses preconditioned conjugate gradient for efficient optimization.

Abstract

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.