Windowing Decomposition Convolutional Neural Network for Image Enhancement

Image enhancement aims to improve the aesthetic quality of images. Most enhancement methods are based on image decomposition techniques. For example, an entire image can be decomposed into a smooth base layer and a residual detail layer. Applying appropriate algorithms to different layers can solve most enhancement problems. Besides decomposing the entire image, the local decomposition approach in local Laplacian filter can also achieve satisfied enhancement results. As a standard convolution is also a local operator that the output values is determined by neighborhood pixels, we observe that the standard convolution can be improved by integrating the local decomposition method for better solving image enhancement problems. Based on this analysis, we propose Windowing Decomposition Convolution (WDC) that decomposes the content of each convolution window by a windowing basic value before applying convolution operation. Using different windowing basic values, the WDC can gather global information and locally separate the processing of different components of images. Moreover, combined with WDC, a new Windowing Decomposition Convolutional Neural Network (WDCNN) is presented. The experimental results show that our WDCNN achieves superior enhancement performance on the MIT-Adobe FiveK and sRGB-SID datasets for noise-free image retouching and low-light noisy image enhancement compared with state-of-the-art techniques.