Abstract:

In the process of image super-resolution reconstruction based on the advanced SwinIR method,there is a problem that the local information modeling ability of low-resolution images is insufficient,resulting in inadequate feature extraction and poor quality of reconstructed images.An improved SwinIR multi-feature fusion image super-resolution reconstruction method is proposed.In the deep feature extraction module,the proposed algorithm first designs several series residual Swin Transformer blocks(RSTB),uses the Swin Transformer layer(STL) of RSTB for long-distance dependent modeling to extract high-frequency image information,and uses residual connections to achieve different levels of feature aggregation.Second,an alternating series spatial attention module and channel attention module(SA-CA) are designed to make up for the lack of local modeling ability of RSTB,so that the network can capture the missing context information on image space and channel dimension,and promote the reconstruction of edge details.Finally,the summation of shallow features and deep features is fused and transmitted to the reconstruction module for high-quality image reconstruction through along jump connection.Experimental results show that in the four test sets with magnifications of 2,3,and 4,the proposed improved algorithm achieves better results than SwinIR in terms of the peak signal-to-noise ratio and structural similarity,and the edge structure and overall contour of the reconstructed image are clearer in terms of visual effects.

Key words: image super-resolution reconstruction, swin transformer, spatial attention, channel attention, multi-feature fusion