Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy

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 pipeline to the idiosyncrasies of endomicroscopy by introducing both: a physically-motivated Voronoi downscaling kernel accounting for the endomicroscope's irregular fibre-based sampling pattern, and realistic noise patterns. We also took advantage of video sequences to exploit a sequence of images for self-supervised zero-shot image quality improvement. We run ablation studies to assess our contribution in regards to the downscaling kernel and noise simulation. We validate our methodology on both synthetic and original data. Synthetic experiments were assessed with reference-based IQA, while our results for original images were evaluated in a user study conducted with both expert and non-expert observers. The results demonstrated superior performance in image quality of ZSSR reconstructions in comparison to the baseline method. The ZSSR is also competitive when compared to supervised single-image SR, especially being the preferred reconstruction technique by experts.

READ FULL TEXT

page 1

page 4

page 7

page 10

research
02/05/2021

Real-World Super-Resolution of Face-Images from Surveillance Cameras

Most existing face image Super-Resolution (SR) methods assume that the L...
research
11/29/2019

Learning from Irregularly Sampled Data for Endomicroscopy Super-resolution: A Comparative Study of Sparse and Dense Approaches

Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) enables perfor...
research
12/17/2017

"Zero-Shot" Super-Resolution using Deep Internal Learning

Deep Learning has led to a dramatic leap in Super-Resolution (SR) perfor...
research
10/12/2022

QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution

Latest advances in Super-Resolution (SR) have been tested with general p...
research
01/21/2019

Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy

In recent years, endomicroscopy has become increasingly used for diagnos...
research
03/02/2022

Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations

In this paper, we consider two challenging issues in reference-based sup...
research
04/23/2020

SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution

In this paper, we tackle a fully unsupervised super-resolution problem, ...

Please sign up or login with your details

Forgot password? Click here to reset