Removing noise from images aka image denoising can be a very challenging task since the
type and amount of noise can greatly vary for each image due to many factors including a …

Blind-spot networks (BSN) have been prevalent network architectures in self-supervised
image denoising (SSID). Existing BSNs are mostly conducted with convolution layers …

Self-supervised learning for image denoising problems in the presence of denaturation for
noisy data is a crucial approach in machine learning. However, theoretical understanding of …

Third harmonic generation (THG) microscopy shows great potential for instant pathology of
brain tumor tissue during surgery. However, due to the maximal permitted exposure of laser …

Over the last decade, supervised denoising models, trained on extensive datasets, have
exhibited remarkable performance in image denoising, owing to their superior denoising …

In recent years, computer vision models have shown a significant improvement in
performance on various image analysis tasks. However, these models are not robust against …

The utilisation of Plug-and-Play (PnP) priors in inverse problems has become increasingly
prominent in recent years. This preference is based on the mathematical equivalence …

Despite the recent upsurge of self‐supervised methods in single image denoising, achieving
robustness and efficiency of performance is still challenging due to some prevalent issues …

Multiplicative noise widely exists in radar images, medical images and other important fields'
images. Compared to normal noises, multiplicative noise has a generally stronger effect on …

Spatiotemporal prediction has garnered significant attention for many years. In recent years,
deep learning methods have emerged as effective models for spatiotemporal data …