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Found 5 papers, 1 papers with code
Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy
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.
Active Annotation of Informative Overlapping Frames in Video Mosaicking Applications
In addition to the efficient construction of a mosaic, our framework provides, as a by-product, ground truth landmark correspondences which can be used for evaluation or learning purposes.
Learning from Irregularly Sampled Data for Endomicroscopy Super-resolution: A Comparative Study of Sparse and Dense Approaches
The main contributions of our study are a comparison of sparse and dense approach in pCLE image reconstruction, implementing trainable generalised NW kernel regression, and adaptation of synthetic data for training pCLE SR.
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction
Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies.
Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy
Robustness to visual challenges during registration and long-range temporal consistency are proposed, offering first positive results on in vivo data for which standard mosaicking techniques are not applicable.
