Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …

This review article provides a comprehensive survey on state-of-the-art impulse and
Gaussian denoising filters applied to images and summarizes the progress that has been …

The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

Image restoration is a long-standing low-level vision problem that aims to restore high-
quality images from low-quality images (eg, downscaled, noisy and compressed images) …

Recently, Transformer architecture has been introduced into image restoration to replace
convolution neural network (CNN) with surprising results. Considering the high …

Compression plays an important role on the efficient transmission and storage of images
and videos through band-limited systems such as streaming services, virtual reality or …

Deep convolutional neural networks perform better on images containing spatially invariant
noise (synthetic noise); however, its performance is limited on real-noisy photographs and …

In this paper, we propose a residual non-local attention network for high-quality image
restoration. Without considering the uneven distribution of information in the corrupted …

In this paper, we propose dense feature fusion (DFF) for image super-resolution (SR). As the
same content in different natural images often have various scales and angles of view …

Invertible networks have various benefits for image denoising since they are lightweight,
information-lossless, and memory-saving during back-propagation. However, applying …