Zero-Reference Low-Light Enhancement via Physical Quadruple Priors
Zero-Reference Low-Light Enhancement via Physical Quadruple Priors
Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters, limiting their ability to handle unseen scenarios. In this paper, we propose a new zero-reference low-light enhancement framework trainable solely with normal …