BFORE: Butterfly-Firefly Optimized Retinex Enhancement for Low-Light Image Quality Improvement

2026-05-05

AI Summary

This study addresses the common challenges in low-light image enhancement—poor visibility, low contrast, and color distortion—by proposing the BFORE framework. Traditional Retinex-based methods often rely on manual parameter tuning and exhibit limited generalization. In contrast, BFORE first transforms images into the HSV color space and applies adaptive gamma correction with weighted distribution (AGCWD) along with adaptive denoising to the value channel. It further introduces a novel hybrid optimization strategy that combines the butterfly optimization algorithm (BOA) and firefly algorithm (FA), employing a switching mechanism to automatically optimize multi-scale Retinex parameters without requiring training data, thus enabling end-to-end enhancement. Experiments on the LOL dataset demonstrate that the proposed method achieves a PSNR of 17.22 dB and an average brightness of 129.97, closely approximating the ideal middle-gray level. It outperforms both conventional approaches and RetinexNet, yielding 12.3% and 14.8% improvements in PSNR and SSIM, respectively, over non-optimized pipelines.

Abstract

Low-light image enhancement is a fundamental challenge in computer vision and multimedia applications, as images captured under insufficient illumination suffer from poor visibility, low contrast, and color distortion. Existing Retinex-based methods rely on manually tuned parameters that fail to generalize across diverse lighting conditions. This paper proposes BFORE (Butterfly-Firefly Optimized Retinex Enhancement), a novel hybrid metaheuristic-optimized framework that automatically tunes the parameters of a multi-stage Retinex-based pipeline. The proposed method converts the input image to HSV color space and applies Adaptive Gamma Correction with Weighted Distribution (AGCWD) to the luminance channel, followed by adaptive denoising. A Butterfly Optimization Algorithm (BOA) optimizes the Multi-Scale Retinex with Color Restoration (MSRCR) parameters, while a Firefly Algorithm (FA) optimizes the AGCWD and denoising parameters. A hybrid BOA-FA switching strategy dynamically balances global exploration and local exploitation. Experimental evaluation on the LOL benchmark dataset (15 paired test images) demonstrates that BFORE achieves the highest PSNR (17.22 dB) among all traditional enhancement methods, with 20.3% improvement over Histogram Equalization and 17.5% over MSRCR. BFORE produces the most naturally balanced mean brightness (129.97), closest to the ideal mid-tone value. Notably, BFORE outperforms RetinexNet -- a deep learning baseline -- in both PSNR (17.22 vs. 16.77 dB) and SSIM (0.5417 vs. 0.4252) without requiring any training data. The hybrid BOA-FA optimization contributes a 12.3% PSNR improvement and 14.8% SSIM improvement over the unoptimized pipeline.

Problem

• low-light image enhancement
• Retinex
• parameter tuning
• image quality
• illumination

Innovation

• Butterfly Optimization Algorithm
• Firefly Algorithm
• Retinex Enhancement
• Metaheuristic Optimization
• Low-Light Image Enhancement