A Perception-inspired Contrast Enhancement Method for Low-light Images in Gradient Domain

Traditional contrast enhancement methods cannot simultaneously compress the dynamic range, adjust the brightness and enhance or preserve the details of images on the low-light images processing.Combining the human visual perception characteristics, a novel contrast enhancement method is proposed to enhance low-light images via a global brightness mapping to local details compensation strategy.Firstly, a nonlinear global brightness mapping model is employed to compress the dynamic range and adjust the overall brightness of the image.Then, the image gradient filed is modified to enhance and restore the local details by combining the luminance masking and suprathreshold contrast perception characteristics of human visual system.Finally a new enhanced image is obtained by using a fast Poisson solver on the target gradient field.The results demonstrate that the proposed algorithm enhances both global and local contrast effectively and improves the visibility of the low-light images.