Zero-shot Adaptive Low Light Enhancement with Retinex Decomposition and Hybrid Curve Estimation

Yuping Xia, Fanjiang Xu, Quan Zheng

Abstract: The low-light image enhancement has long been a critical need in practical applications. Existing methods require either paired or unpaired datasets. Zero-shot methods avoid the requirement of datasets, but they simply enhance the illumination component of the entire image with traditional gamma transformation, which causes color deviation and fails to process low-light images with uneven illumination. Also, these methods often do not take noise into account. We propose a zero-shot low-light image enhancement method. First, we decompose the image into illumination and reflectance according to the Retinex theory. The decomposed reflectance usually contains noise, so we estimate and remove the noise from the reflectance. To enhance the illumination, we design a hybrid illumination enhancement curve that combines gamma transformation and linear transformation. Also, we use a convolutional neural network to estimate the parametric maps in the curve to achieve pixel-level illumination enhancement, so our method can robustly process low-light images with uneven illumination. Extensive experiments demonstrate that our method outperforms recent state-of-the-art methods qualitatively and quantitatively.