A Contrast-Saturation Adaptive Model for Low-Light Image Enhancement

Abstract

Previous Retinex-based methods simultaneously estimate illumination and reflectance, complicating model design and potentially impacting image enhancement due to flawed prior assumptions. In this paper, we explore the physical basis of low-light image enhancement, focusing on contrast, sat-uration, and brightness, and propose an adaptive contrast-saturation (ConSat) model. Our ConSat innovation simplifies model design by focusing solely on a brightness enhancement function, allowing for precise image brightness control while keeping colors natural and realistic. We design a new saturation metric that assesses pixel dispersion from the mean of RGB channels, accurately depicting exposure variations across the image. On this basis, we propose a smart contrast stretching function that adapts contrast adjustments to enhance images under varying light. It boosts contrast in dark, low-saturation areas to clarify details and textures, while curbing it in bright, high-saturation areas to prevent overexposure and color distortion. Finally, we employ a pretrained convolutional neural network (CNN)-based denoiser to achieve satisfactory visual appeal. Numerical experiments show that our ConSat is superior to other state-of-the-art methods in brightness improvement, noise removal, and artifact elimination.