Methods and systems for wavelet domain-based normalizing flow super-resolution image reconstruction

A system for wavelet domain-based normalizing flow super-resolution image reconstruction, wherein the system comprises:

• a building module configured for constructing a training set and a normalizing flow model, wherein the normalizing flow model includes a plurality of levels, each of the plurality of levels including a squeeze layer, two types of conditional mapping layers, a split layer, an activation standard layer, and a quick response (QR) layer;
• a first determination module configured to determine a stable normalizing flow model through a wavelet transform, the QR layer constructed through a principle of orthogonal triangular QR decomposition, and a T-distribution based on the normalizing flow model;
• a second determination module configured to determine a wavelet domain-based normalizing flow super-resolution model by adding a refinement layer based on the stable normalizing flow model, wherein during a training process of the wavelet domain-based normalizing flow super-resolution model, a standard deviation of the T-distribution is dynamically determined based on at least one of a current count of iteration rounds or at least one training sample;
• a training module configured to train the wavelet domain-based normalizing flow super-resolution model based on the training set;
• and
• a reconstruction module configured to reconstruct a super-resolution image based on a trained normalizing flow super-resolution model.