In modern digital microscopy, deconvolution methods are widely used to eliminate a number
of image defects and increase resolution. In this review, we have divided these methods into …

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 …

Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying
nature of defocus blur, characterized by per-pixel point spread functions (PSFs). Existing …

Blind image super-resolution (SR) aims to recover high-resolution (HR) images from low-
resolution (LR) inputs hindered by unknown degradation. Existing blind SR methods exploit …

Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance
Imaging (MRI). In this work, we propose a neural network-based regularization term to …

Non-blind image deconvolution (NBID) is about restoring a latent sharp image from a
blurred one, given an associated blur kernel. Most existing deep neural networks for NBID …

Several researchers have used image enhancement methods to reduce detection errors
and increase verification accuracy in palmprint identification. Divergent opinions exist …

Abstract Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D
sample on the scale of individual cell components. Unfortunately, these 2D images can be …

Generalizability of deep learning models may be severely affected by the difference in the
distributions of the train (source domain) and the test (target domain) sets, eg, when the sets …

Peripheral Difficult Venous Access (PDVA) is a commonplace problem in clinical practice
which results in repetitive punctures, damaged veins, and significant discomfort to the …