Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution
This work addresses the challenge of maintaining structural information in image super-resolution, which is important for visual perception, but it appears incremental as it builds upon existing edge detection and feature fusion techniques.
The paper tackles the problem of restoring low-resolution images to super-resolution images with accurate structural details by proposing Cross-SRN, a lightweight network that uses cross convolution and multi-scale feature fusion to preserve edges, achieving competitive or superior performance against state-of-the-art methods on benchmarks with rich structural textures.
It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details. Existing deep learning works almost neglect the inherent structural information of images, which acts as an important role for visual perception of SR results. In this paper, we design a hierarchical feature exploitation network to probe and preserve structural information in a multi-scale feature fusion manner. First, we propose a cross convolution upon traditional edge detectors to localize and represent edge features. Then, cross convolution blocks (CCBs) are designed with feature normalization and channel attention to consider the inherent correlations of features. Finally, we leverage multi-scale feature fusion group (MFFG) to embed the cross convolution blocks and develop the relations of structural features in different scales hierarchically, invoking a lightweight structure-preserving network named as Cross-SRN. Experimental results demonstrate the Cross-SRN achieves competitive or superior restoration performances against the state-of-the-art methods with accurate and clear structural details. Moreover, we set a criterion to select images with rich structural textures. The proposed Cross-SRN outperforms the state-of-the-art methods on the selected benchmark, which demonstrates that our network has a significant advantage in preserving edges.