Adaptive Single Low-Light Image Enhancement by Fractional Stretching in Logarithmic Domain

Abstract

Low-light image enhancement is a challenging task that aims to improve the visibility and quality of images captured in dark environments. However, existing methods often introduce undesirable artifacts such as color distortion, halo effects, blocking artifacts, and noise amplification. In this paper, we propose a novel method that overcomes these limitations by using the logarithmic domain fractional stretching approach to estimate the reflectance component of the image based on the improved Retinex theory. Moreover, we apply a simple adaptive gamma correction algorithm to the Lab color-space to adjust the brightness and saturation of the image. Our method effectively reduces the impact of non-uniform illumination and produces enhanced images with natural and realistic colors. Extensive experiments across diverse public datasets substantiate the superiority of our method. In both subjective and objective evaluations, our approach outperforms state-of-the-art methods.

Keywords

• Retinex
• image enhancement
• logarithmic transformation
• low-light image
• fractional stretching functions
• non-uniform illumination