Super-resolution image reconstruction from sparsity regularization and deep residual-learned priors

Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work.

This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior.

The new method reconstructs CT images through a reconstruction model incorporating image gradient L0-norm minimization and deep image priors using a plug-and-play super-resolution framework. The deep learning priors are learned from a deep residual network and then plugged into the proposed new framework, and alternating direction method of multipliers is utilized to optimize the iterative solution of the model.

The simulation data analysis results show that the new method improves the signal-to-noise ratio (PSNR) by 7% and the modulation transfer function (MTF) curves show that the value of MTF50 increases by 0.02 factors compared with the result of deep plug-and-play super-resolution. Additionally, the real CT image data analysis results show that the new method improves the PSNR by 5.1% and MTF50 by 0.11 factors.

Both simulation and real data experiments prove that the proposed new CT super-resolution method using deep learning priors can reconstruct CT images with lower noise and better detail recovery. This method is flexible, effective and extensive for low-resolution CT image super-resolution.