Multilevel Feature Exploration Network for Image Superresolution

Image superresolution (SR) is a classical issue in computer vision area. Recently, there are elaborated convolutional neural networks (CNNs) demonstrating remarkable effectiveness on image SR. However, most of the previous works lack effective exploration on the structural information, which plays a critical role for image quality. In this paper, we find that the hierarchical design can effectively restore the structural information and devise a multilevel feature exploration network for image SR (MFSR). Specially, we design an encoder-decoder architecture to concentrate on structural information from different levels and devise a spatial attention mechanism to address the inherent correlation among features for effective restoration. Experimental results show the proposed MFSR can restore more correct edges and lines and achieves both better objective and subjective performances than the state-of-the-art methods with higher PSNR/SSIM results, indicating the effectiveness on structural information restoration.