Multi Morphological Sparse Regularized Image Super-Resolution Reconstruction Based on Machine Learning Algorithm

The traditional image super-resolution (SR) reconstruction methods do not carry out sparse coding when performing SR reconstruction, resulting in poor structural similarity of image SR reconstruction. Therefore, this paper proposes a a super-resolution reconstruction method for polymorphic sparsity regularized images based on machine learning algorithms and builds a sparse representation model of polymorphic regularized images. For sparse representation models with different norm constraints, the orthogonal matching tracking in the greedy algorithm is used perform sparse coding to solve the sparse representation coefficient. The least square method in machine learning algorithm is used to solve the inverse operation of dictionary matrix update, construct the image super-resolution reconstruction minimization objective function, and solve the loss function between the super-resolution reconstruction image and the actual image, so as to complete the super-resolution reconstruction of polymorphic sparsity regularized image. The experimental results show that the edge recovery of the image under the proposed method is clearer, and the edge with more distinct edges and corners can be reconstructed. The Peak-Signal-to-Noise Ratio (PSNR) of the super-resolution reconstruction image is 60.5dB, and the Structural Similarity (SSIM) of the super-resolution reconstruction image is as high as 0.99, which effectively improves the image reconstruction effect.