Research on the Improvement of Image Super Resolution Reconstruction Algorithm Based on AWSRN Model

In domains such as medical diagnostics, surveillance technology, and geospatial imaging, the escalating need for ultra-high-definition imagery has exposed the limitations of conventional super-resolution methods. These legacy algorithms often fail to deliver the precision and clarity demanded by modern applications. Therefore, this article proposes an optimization algorithm based on the AWSRN network model, aiming to achieve efficient image superresolution reconstruction, reduce computational costs, and enhance image realism. Firstly, optimize the internal structure of the network and enhance its feature extraction and fusion capabilities; Secondly, to enhance feature extraction precision, a novel module integrating depth-separable convolution with an attention-based mechanism is proposed. Additionally, a hybrid loss function-merging perceptual quality metrics with adversarial training objectives-is employed to rigorously evaluate the disparity between generated and groundtruth images. The MPTS training strategy further optimizes convergence efficiency. Empirical evaluations demonstrate that the enhanced AWSRN model achieves substantial improvements over its baseline counterpart across multiple upscaling factors, particularly at 4 × magnification. Specifically, on the Urban100 benchmark, the proposed method elevates PSNR by 1.06 points and SSIM by 0.0239, while maintaining computational efficiency. These advancements offer valuable insights for high-fidelity image upscaling methodologies.