Computed Tomography Image Restoration Using a Quantum-Based Deep Unrolled Denoiser and a Plug-and-Play Framework

In this work, we address the problem of cone beam computed tomography (CBCT) image resolution enhancement by exploiting a newly introduced deep unrolled quantum denoiser, based on quantum interaction theory adapted to computational imaging. Following recent advances in image restoration using the Plug-and-Play (PnP) framework, we impose this external deep learning denoiser as a regularizer within the super-resolution (SR) problem. The quantum-based deep unrolled denoiser combined with a computationally efficient way to deal with the degradation operators, and the PnP scheme, result in an original way of approaching the image resolution enhancement problem. Experiments conducted on dental CBCT images are presented to illustrate the efficiency of the proposed model for image resolution enhancement tasks. The numerical results suggest that the proposed method allows better restoration performances compared to existing state-of-the-art approaches.