Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

With the explosion in the number of digital images taken every day, the demand for more
accurate and visually pleasing images is increasing. However, the images captured by …

Deep convolutional neural networks perform better on images containing spatially invariant
noise (synthetic noise); however, its performance is limited on real-noisy photographs and …

We consider the single image super-resolution problem in a more general case that the low-
/high-resolution pairs and the down-sampling process are unavailable. Different from …

At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …

In surveillance, monitoring and tactical reconnaissance, gathering visual information from a
dynamic environment and accurately processing such data are essential to making informed …

Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable
phenomena being limited by the optics of the microscope, the chemistry of the fluorophores …

We propose to restore old photos that suffer from severe degradation through a deep
learning approach. Unlike conventional restoration tasks that can be solved through …

Most of existing image denoising methods assume the corrupted noise to be additive white
Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much …

While deep learning methods have achieved state-of-the-art performance in many
challenging inverse problems like image inpainting and super-resolution, they invariably …