Image denoising faces significant challenges, arising from the sources of noise. Specifically,
Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in …

Deep convolutional networks–based super-resolution is a fast-growing field with numerous
practical applications. In this exposition, we extensively compare more than 30 state-of-the …

Single image super-resolution (SISR) has witnessed great strides with the development of
deep learning. However, most existing studies focus on building more complex networks …

Single image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from
given low-resolution (LR) images. It is an ill-posed problem because one LR image …

In recent years, single image super-resolution (SISR) methods using deep convolution
neural network (CNN) have achieved impressive results. Thanks to the powerful …

Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given
low-resolution image. This fundamental fact is largely ignored by state-of-the-art deep …

Recently data-driven image inpainting methods have made inspiring progress, impacting
fundamental image editing tasks such as object removal and damaged image repairing …

Super resolution (SR) methods typically assume that the low-resolution (LR) image was
downscaled from the unknown high-resolution (HR) image by a fixedideal'downscaling …

Blind image super-resolution (SR), aiming to super-resolve low-resolution images with
unknown degradation, has attracted increasing attention due to its significance in promoting …

This paper introduces a novel large dataset for video deblurring, video super-resolution and
studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges …