METHODS AND MODELS FOR IMAGE ENHANCEMENT IN LOW-LIGHT CONDITIONS

Abstract

This article reviews current research on various approaches to improving images captured in low-light or dimly lit conditions. To address this issue, an adaptive ensemble method is proposed that combines several, specifically, four classical image processing algorithms and automatically distributes weights among them based on local quality. The developed system uses CLAHE, gamma correction, the Retinex algorithm, and automatic contrast stretching as its basic components. A comparative analysis of the developed method with the modern Deep Learning approach, Zero-DCE, was conducted on the real-world LOL dataset. The experimental results show that the proposed classical ensemble approach achieves a PSNR of 17.91 dB, outperforming the machine learning model by 14.2 percent, while the method does not require a training phase on datasets or GPU computations.