Plug-and-Play gradient-based denoisers applied to CT image enhancement
This addresses image quality issues in CT scans for medical diagnosis, representing an incremental improvement with a new gradient-domain approach.
The paper tackled the problem of blur and noise in CT images by proposing a novel gradient-based Plug-and-Play algorithm using internal and external denoisers, achieving remarkable enhancements compared to state-of-the-art methods in simulated and real medical settings.
Blur and noise corrupting Computed Tomography (CT) images can hide or distort small but important details, negatively affecting the diagnosis. In this paper, we present a novel gradient-based Plug-and-Play algorithm, constructed on the Half-Quadratic Splitting scheme, and we apply it to restore CT images. In particular, we consider different schemes encompassing external and internal denoisers as priors, defined on the image gradient domain. The internal prior is based on the Total Variation functional. The external denoiser is implemented by a deep Convolutional Neural Network (CNN) trained on the gradient domain (and not on the image one, as in state-of-the-art works). We also prove a general fixed-point convergence theorem under weak assumptions on both internal and external denoisers. The experiments confirm the effectiveness of the proposed framework in restoring blurred noisy CT images, both in simulated and real medical settings. The achieved enhancements in the restored images are really remarkable, if compared to the results of many state-of-the-art methods.