Zero-Shot Super-Resolution With a Physically-Motivated Downsampling Kernel for Endomicroscopy.

ABSTRACT: Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA). Therefore, alternative methods, such as unsupervised SR are being explored. To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images. We tailored the proposed p

SUBMITTER: Szczotka AB 

PROVIDER: S-EPMC7610492 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature