Image denoising aims to restore a clean image from an observed noisy one. Model-based
image denoising approaches can achieve good generalization ability over different noise …

Convolutional neural networks (CNNs) have been tremendously successful in solving
imaging inverse problems. To understand their success, an effective strategy is to construct …

Deep dictionary learning (DDL) aims to learn dictionaries at different levels and the deepest
level representations. However, existing DDL algorithms impose a-norm constraint on the …

Deep dictionary learning (DDL) can mine deeper representations of data more effectively
than single-layer dictionary learning. However, existing DDL methods with specific sparse …

The existing single dictionary learning algorithms are applied to face recognition and
achieve satisfactory results. However, their performance is poor when dealing with noisy …

In this paper, we propose an invertible neural network for image denoising (DnINN) inspired
by the transform-based denoising framework. The proposed DnINN consists of an invertible …

Neighbor embedding (NE) is a widely used super-resolution (SR) algorithm, but the one-to-
many problem always degrades the performance of NE. The simplest way to avoid this …

Adversarial and periodic training method plays an essential role in video super-resolution,
which can generate spatial high frequency detail and temporal consistency relation …

Sparse representation‐based non‐local self‐similarity approaches have demonstrated
promising performance in single image super‐resolution reconstruction. This type of …

Learning‐based superresolution reconstruction is an efficient image processing technique
that has become a popular topic in recent years. Since superresolution is an ill‐conditioned …