Bright-Channel Retinex Enhancement with a Conditional Overdispered-Noise Analysis

2026-08-10

AI Summary

This work addresses the heteroscedastic noise amplification inherent in Retinex-based low-light image enhancement by proposing an unsupervised, non-learning method. The approach integrates bright channel prior–based illumination estimation, Retinex reflectance decomposition, and edge-preserving denoising, and—within a Retinex framework—introduces for the first time a conditional negative binomial pseudo-count model to characterize the over-dispersed noise induced by division operations. It further provides a boundary-constrained maximum likelihood solution for zero-valued observations, eliminating the need for sensor calibration or deep learning. Experimental results demonstrate state-of-the-art performance among traditional methods on the LOL-v1 dataset, achieving 17.74 dB PSNR and 0.739 SSIM, while attaining real-time processing at 43 FPS on an Apple M2 Pro for 600×400 resolution images.

Abstract

I present a training-free low-light enhancement method that combines local bright-channel illumination estimation, Retinex division, and edge-preserving denoising. For a fixed illumination estimate, a conditional Negative -Binominal psueduo-count method characterises the heteroscedastic noise amplified by division. The unconstrained reflectance ratio is the pixelwise maximum-likelihood estimate, with a boundary solution for zero-valued observations; the implemented estimate additionally applies illumination filtering and range clipping. The NB model is a diagnostic noise analysis rather than a calibrated sensor model, and the final fixed-bandwidth bilateral filter is an empirical approximation rather than the exact Bayesian solution. On the LOL-v1 dataset, the methodobtains mean PSNR/SSIM of 17.74dB/0.739, the highest values among the evaluated with conventional methods. A 400X600 image is processed at approximately 43 FPS on an Apple M2 Pro CPU.

Problem

• low-light enhancement
• heteroscedastic noise
• Retinex decomposition
• noise amplification
• illumination estimation

Innovation

• Bright-Channel Retinex
• Conditional Negative-Binomial Noise
• Heteroscedastic Noise Modeling
• Training-Free Enhancement
• Edge-Preserving Denoising