Abstract: The present disclosure discloses a method and a system for wavelet domain-based normalizing flow super-resolution image reconstruction. The method includes 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; determining a stable normalizing flow model through a wavelet transform, a reconstructed QR layer, and a T-distribution based on the normalizing flow model; determining a wavelet domain-based normalizing flow super-resolution model by adding a refinement layer based on the stable normalizing flow model; training the wavelet domain-based normalizing flow super-resolution model based on the training set; and reconstructing a super-resolution image based on a trained normalizing flow super-resolution model.