Image super-resolution reconstruction technology and performance improvement strategy integrating MSA and MSRISR

This study proposes a novel architecture grounded in multi-scale attention and multi-scale residual super-resolution network to improve the efficacy of image super-resolution reconstruction technology. The study introduces an adaptive local attention mechanism and a channel-space collaborative gating mechanism to improve the cross-scale transfer efficiency of key features. During the experiment, the introduced approach achieves peak signal-to-noise ratios of 32.98 dB and 30.76 dB with convolution kernels of 3, 7, and 11, and structural similarities of 0.891 and 0.932. The accuracy of detail feature extraction reaches 98.7%. The introduced approach exhibits excellent stability and reconstruction quality. Compared with other conventional algorithms, the introduced approach exhibits significant advantages, achieving a detail feature extraction accuracy of 98.7%. From the above results, the novel strategy proposed by the research can improve the quality of super-resolution reconstruction and has high practical value.