Quantum Denoising-Based Super-Resolution Algorithm Applied To Dental Tomography Images

Enhancing the spatial resolution of an image is an important field of research in number of applications including medical ones. In this paper, we address the super-resolution (SR) problem exploiting a newly introduced adaptive quantum denoiser which is based on quantum interaction theory applied in an imaging context. In particular, following recent developments, we impose this external denoiser as a prior function within the Plug-and-Play (PnP) and Regularization by Denoising (RED) approaches. This quantum denoiser combined with, on the one hand, a computationally efficient way of handing both decimation and blur operators, and on the other hand PnP and RED schemes, shows an original way of solving the SR problems. Dental computed tomography images are used to illustrate the potential of the proposed algorithms for high-resolution image retrieval. Numerical experiments show that the proposed methods provide comparable or slightly better results than existing methods.