关键词: 低照度图像, 透射率, 亮通道先验, 自适应, 交替迭代最优化

Abstract: The low light images have very low contrast and strong noise. To solve the problem, an adaptive variational model for low light image enhancement is proposed. The transmission of the low light image after inversion is rough estimated based on the luminance component, and the transmission is further detailed by using the Retinex algorithm in order to retain more image details. In order to suppress the amplification of the noise and keep the edges, the regularization parameters are adaptively adjusted according to the priori of the bright channel and the local variance. The optimal solution of energy functional equation including transmittance and restored image is obtained by alternating iterative optimization method. Experimental results show that the proposed model can effectively enhance low light image, retain more details, and suppress noise amplification. Compared with[l1]norm regularization method, the larger the image size, this model has higher computational efficiency and more obvious advantage of computing time.

Key words: low light image, transmission, bright channel prior, adaptive, alternating iterative optimization