Low-light image enhancement via channel-wise intensity transformation

Abstract

We propose a low-light image enhancement algorithm that learns channel-wise transformation functions. First, we develop a lightweight network, called the transformation function estimation network (TFE-Net), to predict the channel-wise transformation functions. TFE-Net learns to generate the transformation functions by considering both the global and local characteristics of the input image. Then, we obtain enhanced images by performing channel-wise intensity transformation. Experimental results show that the proposed algorithm provides higher image quality than conventional algorithms.