Subband Adaptive Image Deblocking Using Wavelet Based Convolutional Neural Networks

In this paper, we propose subband adaptive image deblocking using wavelet based convolutional neural networks (CNNs). We build wavelet based CNNs for image deblocking to achieve subband adaptive reconstruction. First, we perform subband adaptive processing after the discrete wavelet transform (DWT) on the input image. For low frequency subband (LL), we use a simple and effective shallow CNN to restore the low frequency component, while for high frequency subbands (LH, HL, and HH) we utilize multi-kernel convolution to capture multiscale features and restore sparse high frequency components. Then, we conduct mixed convolution of dilated convolution and standard convolution to expand the receptive field while introducing channel and spatial attentions to adjust the proportion of different subbands and spatial coordinates. Various experiments on Classic5 and LIVE1 datasets show that the proposed method successfully recovers sharp edges and clear textures in highly compressed images while removing compression artifacts such as blocking and banding. Moreover, the proposed method achieves comparable state-of-the-art performance on compression artifact removal in terms of both visual quality and quantitative measurements.