Image deblocking based on multi-scale wide-activated residual attention network

Abstract:

To save transmission bandwidth and storage resources, imaging devices and systems generally perform lossy compression on images and videos. JPEG images usually suffer from obvious blocking effect due to block quantization coding. Removing the blocking effect of the image can not only improve the visual experience of users, but also facilitate other computer vision tasks. Therefore, an image deblocking method based on multi-scale wide-activated residual attention network (MWRAN) is proposed. The MWRAN is mainly constructed by the multi-scale wide-activated residual attention block (MWRAB). The MWRAB can not only activate more non-linear features to promote the flow of information in the network, but also capture rich image multi-scale features. In addition, the MWRAB can adaptively adjust the learned features to focus on more important information via the proposed lightweight contrast-aware channel attention (LCCA). The ablation experiment is conducted to verify the effectiveness of the proposed MWRAB. The MWRAN achieves higher objective evaluation indices and produces subjective perceptual effects closer to the original image than several state-of-the-art image deblocking methods on common benchmark datasets.