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 …

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 …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …

The learning of the deep networks largely relies on the data with human-annotated labels. In
some label insufficient situations, the performance degrades on the decision boundary with …

Real-world image noise removal is a long-standing yet very challenging task in computer
vision. The success of deep neural network in denoising stimulates the research of noise …

Blind image denoising is an important yet very challenging problem in computer vision due
to the complicated acquisition process of real images. In this work we propose a new …

Noise causes unpleasant visual effects in low-light image/video enhancement. In this paper,
we aim to make the enhancement model and method aware of noise in the whole process …

Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …

Deep neural networks (DNNs) have shown very promising results for various image
restoration (IR) tasks. However, the design of network architectures remains a major …