Zero-Shot Super-Resolution With a Physically-Motivated Downsampling Kernel for Endomicroscopy
| Autor: | Matthew J. Clarkson, Stephen P. Pereira, Dzhoshkun I. Shakir, Agnieszka Barbara Szczotka, Tom Vercauteren |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Image quality Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Iterative reconstruction Convolutional neural network Article Machine Learning (cs.LG) 030218 nuclear medicine & medical imaging Upsampling 03 medical and health sciences 0302 clinical medicine FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Endomicroscopy Humans Computer Simulation Electrical and Electronic Engineering Ground truth Radiological and Ultrasound Technology business.industry Image and Video Processing (eess.IV) Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing Computer Science Applications Kernel (image processing) 020201 artificial intelligence & image processing Noise (video) Artificial intelligence Neural Networks Computer business Software |
| Zdroj: | IEEE Trans Med Imaging |
| ISSN: | 1558-254X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|