Image Restoration via Deep Memory-Based Latent Attention Network

Deep convolutional neural network (CNN) has made impressive achievements in the field of image restoration. However, most of deep CNN-based models have limited capability of utilizing the hierarchical features and these features are often treated equally, thus restricting the restoration performance. To address this issue, the present work proposes a novel memory-based latent attention network (MLANet) aiming to effectively restore a high-quality image from a corresponding low-quality one. The key idea of this work is to employ a memory-based latent attention block (MLAB), which is stacked in MLANet and makes better use of global and local features through the network. Specifically, the MLAB contains a main branch and a latent branch. The former is used to extract local multi-level features, and the latter preserves global information by the structure within a latent design. Furthermore, a multi-kernel attention module is incorporated into the latent branch to adaptively learn more effective features with mixed attention. To validate the effectiveness and generalization ability, MLANet is evaluated on three representative image restoration tasks: image super-resolution, image denoising, and image compression artifact reduction. Experimental results show that MLANet performs better than the state-of-the-art methods on all the tasks.