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Blur and noise corrupting Computed Tomography (CT) images can hide or distortsmall but important details, negatively affecting the diagnosis. In this paper,we present a novel gradient-based Plug-and-Play algorithm, constructed on theHalf-Quadratic Splitting scheme, and we apply it to restore CT images. Inparticular, we consider different schemes encompassing external and internaldenoisers as priors, defined on the image gradient domain. The internal prioris based on the Total Variation functional. The external denoiser isimplemented by a deep Convolutional Neural Network (CNN) trained on thegradient domain (and not on the image one, as in state-of-the-art works). Wealso prove a general fixed-point convergence theorem under weak assumptions onboth internal and external denoisers. The experiments confirm the effectivenessof the proposed framework in restoring blurred noisy CT images, both insimulated and real medical settings. The achieved enhancements in the restoredimages are really remarkable, if compared to the results of manystate-of-the-art methods.

Plug-and-Play gradient-based denoisers applied to CT image enhancement