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

Pasquale Cascarano, Elena Loli Piccolomini, Elena Morotti and Andrea Sebastiani

Applied Mathematics and Computation, 2022, vol. 422, issue C

Abstract: Blur and noise corrupting Computed Tomography (CT) images can hide or distort small but important details, negatively affecting the consequent diagnosis. In this paper, we present a novel gradient-based Plug-and-Play (PnP) algorithm and we apply it to restore CT images. The plugged denoiser is implemented as a deep Convolutional Neural Network (CNN) trained on the gradient domain (and not on the image one, as in state-of-the-art works) and it induces an external prior onto the restoration model. We further consider a hybrid scheme which combines the gradient-based external denoiser with an internal one, obtained from the Total Variation functional. The proposed frameworks rely on the Half-Quadratic Splitting scheme and we prove a general fixed-point convergence theorem, under weak assumptions on both the denoisers. The experiments confirm the effectiveness of the proposed gradient-based approach in restoring blurred noisy CT images, both in simulated and real medical settings. The obtained performances outperform the achievements of many state-of-the-art methods.

Keywords: Deblur and denoise; Plug-and-Play; Gradient-based regularization; External-internal image priors; CNN Denoisers; Computed tomography imaging (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:422:y:2022:i:c:s0096300322000534

DOI: 10.1016/j.amc.2022.126967

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