Low-Light Image Enhancement Based On Modified U-Net
Yuantian Cai, U KinTak · 2019
Recent years, researches in low-light image enhancement has done quite a lot and shown great success in real life application. In this paper, a modified U-Net-based method is proposed by combining with Recurrent Residual Convolutional Units (RRCU) and Dilated Convolution. In our method, we achieve higher accuracy by three enhancements. Firstly, replace the basic 3×3 convolution blocks with RRCU. Secondly, replace the 3×3 convolution bottle neck block with multi-ways concatenation. Lastly, replace the max pooling operation with dilated convolution between upper and lower levels. In experiment, the performance of the proposed modified U-Net network is proved to obtain obviously better accuracy than other existing methods in low-light image enhancement.