Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In
blind settings, the degradation kernel or the noise level are unknown. This makes restoration …

We propose to leverage denoising autoencoder networks as priors to address image
restoration problems. We build on the key observation that the output of an optimal …

Recent state-of-the-art super-resolution methods have achieved impressive performance on
ideal datasets regardless of blur and noise. However, these methods always fail in real …

While deep neural networks exhibit state-of-the-art results in the task of image super-
resolution (SR) with a fixed known acquisition process (eg, a bicubic downscaling kernel) …

Recent advances in deep learning have led to significant improvements in single image
super-resolution (SR) research. However, due to the amplification of noise during the …

This paper presents a new variational inference framework for image restoration and a
convolutional neural network (CNN) structure that can solve the restoration problems …

While researches on model-based blind single image super-resolution (SISR) have
achieved tremendous successes recently, most of them do not consider the image …

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 …

We propose a state-of-the-art method for super-resolution with non-uniform blur. Single-
image super-resolution methods seek to restore a high-resolution image from blurred …

Image super-resolution (SR) research has witnessed impressive progress thanks to the
advance of convolutional neural networks (CNNs) in recent years. However, most existing …