Low-Light Image Enhancement via Poisson Noise Aware Retinex Model
Xiangyu Kong, Lei Liu, Yunsheng Qian
Abstract
Limited by the number of available photons collected by a pixel and the stability of the imaging system, images captured under poor illumination conditions are often degraded by heavy shot noise and low contrast. In this letter, we propose a novel low-light image enhancement algorithm using the Poisson noise-aware Retinex model, in which the Poisson distribution is considered to formulate the fidelity term in the Retinex model for the first time. Furthermore, the space-variant weight maps for total variation regularization terms are calculated according to the piecewise smooth prior of illumination component and the Poisson noise distribution prior, which contributes to noise suppression in different noise intensity while preserving image details and structures. We get the optimal solution of the Poisson noise-aware Retinex model by an iterative optimization algorithm. Finally, the enhanced image is obtained by gamma correcting the estimated illumination map. Experimental results demonstrate that the proposed model performs better than state-of-the-art methods in low-light image enhancement and noise suppression.