A New Framework for Retinex based Color Image Enhancement using Particle Swarm Optimization

TL;DR

This paper introduces a novel framework that uses Particle Swarm Optimization to automatically tune parameters of the MultiScale Retinex algorithm, significantly improving color image enhancement quality and efficiency.

Contribution

It presents a new PSO-based method for optimizing MSR parameters, leading to better image quality and reduced computation time compared to existing approaches.

Findings

Enhanced images are clearer and more vivid.

The method outperforms existing techniques in quality metrics.

Significant reduction in parameter tuning effort.

Abstract

A new approach for tuning the parameters of MultiScale Retinex (MSR) based color image enhancement algorithm using a popular optimization method, namely, Particle Swarm Optimization (PSO) is presented in this paper. The image enhancement using MSR scheme heavily depends on parameters such as Gaussian surround space constant, number of scales, gain and offset etc. Selection of these parameters, empirically and its application to MSR scheme to produce inevitable results are the major blemishes. The method presented here results in huge savings of computation time as well as improvement in the visual quality of an image, since the PSO exploited maximizes the MSR parameters. The objective of PSO is to validate the visual quality of the enhanced image iteratively using an effective objective criterion based on entropy and edge information of an image. The PSO method of parameter optimization of MSR scheme achieves a very good quality of reconstructed images, far better than that possible with the other existing methods. Finally, the quality of the enhanced color images obtained by the proposed method are evaluated using novel metric, namely, Wavelet Energy (WE). The experimental results presented show that color images enhanced using the proposed scheme are clearer, more vivid and efficient.