Research on Image Super-Resolution Reconstruction Method Based on Deep Convolutional Neural Network

Abstract

As an ill-posed inverse problem, image super-resolution reconstruction relies on effective modeling of natural image priors for its solution. Deep convolutional neural networks provide a new path to break through the limitations of traditional hand-crafted features. This paper systematically studies the theoretical framework of this direction: at the mathematical foundation level, it analyzes the degradation linear model and the ill-posedness of the inverse problem, and clarifies the transformation characteristics of convolution mapping and the constraints of receptive field; at the feature reuse level, it explores the gradient preservation of residual paths, the feature reuse of dense connections, and the recalibration mechanism driven by channel attention; at the reconstruction strategy level, it elaborates the Laplacian pyramid decomposition, the sub-pixel convolution shuffling operation, and the recursive back-projection error correction method. The above content constructs a theoretical framework of deep convolutional neural networks for super-resolution from three dimensions: network architecture, information flow, and multi-scale reconstruction.