A light weight image super-resolution algorithm

At present, most image super-resolution reconstruction algorithms improve the reconstruction ability of the algorithm by increasing the depth or width of the convolutional neural network. Although these algorithms can improve the reconstruction effect to a certain extent, the algorithm complexity is high. In order to improve this problem, a lightweight super-resolution reconstruction algorithm based on attention mechanism and feature fusion is proposed. Based on the pixel attention mechanism, pixels with different characteristics are weighted. Subsequently, a multi-level feature fusion module is designed based on the lightweight channel attention mechanism module, and further uses the global residual connection to perform global feature fusion. Finally, sub-pixel convolution up-sampling is performed on the extracted features. The algorithm is tested on four commonly used public data sets in the field of image super-resolution reconstruction, Set5, Set14, BSD100, and Urban100. The experimental results show that compared with others algorithms, in terms of image evaluation indicators, it has a higher peak signal-to-noise ratio and structural similarity. In terms of visual effects, compared with the reconstructed image of the contrast algorithm, the image reconstructed by this algorithm has richer details.At the same time, compared with the comparison algorithm, the proposedalgorithm has the least amount of parameters.