Blind Image Reconstruction using Deep Gaussian Mixture Learning

Image reconstruction plays an important role in modern electronic information systems and equipment and has attracted widespread intense attention in academic and industrial fields. Restoring delicate image particulars using traditional handcrafted prior image reconstruction methods is often difficult because of the inferior prior characterization abilities of these methods. In this article, an efficient image reconstruction model, named DGMR, is proposed based on deep Gaussian mixture learning. In particular, a channel attention mechanism is designed for image spatial correlation exploitation. Both the external and internal information are explored using deep Gaussian mixture. A residual Swin transformer module is constructed to learn Gaussian mixture priors, including the images ’ means and variances; in contrast, existing methods calculate only the mean and ignore the variance. Moreover, sparse regularized united learning is developed to improve invariant representation learning ability, and the model is customized by internal learning with spatial constraints and regularization. Extensive qualitative and quantitative experiments are performed, confirming that DGMR is superior to the current advanced comparison approaches.