Image super-resolution reconstruction based on attention mechanism and feature fusion
This work improves image super-resolution for applications like medical imaging or photography, but it appears incremental as it builds on existing attention and fusion techniques.
The authors tackled the problem of image super-resolution by addressing the neglect of inherent image attributes and single-scale feature extraction in convolutional neural networks, proposing a network that integrates attention mechanisms and multi-scale feature fusion to achieve better performance in objective metrics and visual quality compared to other algorithms.
Aiming at the problems that the convolutional neural networks neglect to capture the inherent attributes of natural images and extract features only in a single scale in the field of image super-resolution reconstruction, a network structure based on attention mechanism and multi-scale feature fusion is proposed. By using the attention mechanism, the network can effectively integrate the non-local information and second-order features of the image, so as to improve the feature expression ability of the network. At the same time, the convolution kernel of different scales is used to extract the multi-scale information of the image, so as to preserve the complete information characteristics at different scales. Experimental results show that the proposed method can achieve better performance over other representative super-resolution reconstruction algorithms in objective quantitative metrics and visual quality.