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

arXiv: https://arxiv.org/abs/2307.02625 · HTML: https://mlsanity.com/paper/2307.02625/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model](https://mlsanity.com/paper/1803.07252/) (2018)
- [Graph Spectral Image Processing](https://mlsanity.com/paper/1801.04749/) (2018)
- [Joint Enhancement and Denoising Method via Sequential Decomposition](https://mlsanity.com/paper/1804.08468/) (2018)
- [Signal Recovery on Graphs: Variation Minimization](https://mlsanity.com/paper/1411.7414/) (2014)
- [Graph-Based Blind Image Deblurring From a Single Photograph](https://mlsanity.com/paper/1802.07929/) (2018)
- [Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain](https://mlsanity.com/paper/1604.07948/) (2016)
- [Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images](https://mlsanity.com/paper/1607.01895/) (2016)
- [Unrolling of Deep Graph Total Variation for Image Denoising](https://mlsanity.com/paper/2010.11290/) (2020)
- [Manifold Graph Signal Restoration using Gradient Graph Laplacian Regularizer](https://mlsanity.com/paper/2206.04245/) (2022)
- [Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images](https://mlsanity.com/paper/1804.02554/) (2018)
- [Bias-Variance Tradeoff of Graph Laplacian Regularizer](https://mlsanity.com/paper/1706.00544/) (2017)

## Built on by (most cited first, 2 of 2)

- [CircuitSteer: Geometrically Aligned Multi-Layer Steering via Sparse Autoencoder Circuits](https://mlsanity.com/paper/2608.05732/) (2026)
- [Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer](https://mlsanity.com/paper/2409.06676/) (2024)

## Abstract (arXiv)

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.