Single low-light image enhancer using Taylor expansion and fully dynamic convolution
Chenggang Dai, Zhiguang Guan, Mingxing Lin · Signal Processing · 2021
Most preexisting deep learning-based enhancers are incapable of adjusting brightness of enhanced images, due to constant convolutional kernels. To address this issue, we propose an enhancer based on Taylor expansion and fully dynamic convolution, which can flexibly adjust the level of the brightness. In this study, the retinex model is first modified to serve as a framework for the proposed enhancer. Next, Taylor expansion and the attention mechanism are applied to construct a backbone network based on the modified retinex model. Subsequently, a strategy of fully dynamic convolution is proposed to flexibly adjust the degree of the brightness. Specifically, a weight-bias learning network is designed to dynamically generate weight matrices which are fed to the backbone network to perform the dynamic convolution. Furthermore, local mean and variance are used as a supplemental term for our loss function to improve the performance of the proposed enhancer, while a method of simulating realistic low-light images is used for synthesizing training data to suppress noise. Comprehensive experiments demonstrate satisfactory performance of the proposed enhancer in improving the clarity of low-light images and adjusting the degree of the brightness flexibly.