The computer-based approach is required for the non-invasive detection of chronic liver
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …

We present a new end-to-end generative adversarial network (GAN) for single image motion
deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring …

Underwater image enhancement has been attracting much attention due to its significance
in marine engineering and aquatic robotics. Numerous underwater image enhancement …

Diffusion model-based inverse problem solvers have demonstrated state-of-the-art
performance in cases where the forward operator is known (ie non-blind). However, the …

Blind deconvolution is a classical yet challenging low-level vision problem with many real-
world applications. Traditional maximum a posterior (MAP) based methods rely heavily on …

Blind image deblurring aims to recover sharp image from a blurred one while the blur kernel
is unknown. To solve this ill-posed problem, a great amount of image priors have been …

We present an effective semi-supervised learning algorithm for single image dehazing. The
proposed algorithm applies a deep Convolutional Neural Network (CNN) containing a …

Blind image deblurring remains a topic of enduring interest. Learning based approaches,
especially those that employ neural networks have emerged to complement traditional …

Motion deblurring is one of the fundamental problems of computer vision and has received
continuous attention. The variability in blur, both within and across images, imposes …

The production of a high spatial resolution (HR) hyperspectral image (HSI) through the
fusion of a low spatial resolution (LR) HSI with an HR multispectral image (MSI) has …