Imposing consistency through proxy tasks has been shown to enhance data-driven learning
and enable self-supervision in various tasks. This paper introduces novel and effective …

Over the last decade, the number of digital images captured per day has increased
exponentially, due to the accessibility of imaging devices. The visual quality of photographs …

Deep convolutional neural networks (CNNs) for video denoising are typically trained with
supervision, assuming the availability of clean videos. However, in many applications, such …

In this paper, we present a novel robust framework for low-level vision tasks, including
denoising, object removal, frame interpolation, and super-resolution, that does not require …

High-resolution satellite imagery is a key element for many Earth monitoring applications.
Satellites such as Sentinel-2 feature characteristics that are favorable for super-resolution …

Recent constellations of optical satellites are adopting multi-image super-resolution (MISR)
from bursts of push-frame images as a way to increase the resolution and reduce the noise …

Deep learning has become a prominent tool for video denoising. However, most existing
deep video denoising methods require supervised training using noise-free videos …

Noise interference during the acquisition of digital images can severely degrade image
quality, particularly for images captured under low-light conditions; however, the removal of …

Abstract Background and Objective: Accurate segmentation of electron microscopy (EM)
volumes of the brain is essential to characterize neuronal structures at a cell or organelle …

Self-supervised video denoising has seen decent progress through the use of blind spot
networks. However, under their blind spot constraints, previous self-supervised video …