A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction

arXiv:2402.18147v1 Announce Type: cross
Abstract: Human vision relies heavily on available ambient light to perceive objects. Low-light scenes pose two distinct challenges: information loss due to insufficient illumination and undesirable brightness shifts. Low-light image enhancement (LLIE) refers to image enhancement technology tailored to handle this scenario. We introduce CPGA-Net, an innovative LLIE network that combines dark/bright channel priors and gamma correction via deep learning and integrates features inspired by the Atmospheric Scattering Model and the Retinex Theory. This approach combines …

abstract ambient arxiv challenges cs.cv eess.iv human image information light loss low network objects prior technology type via vision