Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …

Inspired by the great success of deep neural networks (DNNs), many unfolding methods
have been proposed to integrate traditional image modeling techniques, such as dictionary …

Intelligent fault diagnosis (IFD) has experienced tremendous progress owing to a great deal
to deep learning (DL)-based methods over the decades. However, the “black box” nature of …

Recently, regression analysis has become a popular tool for face recognition. Most existing
regression methods use the one-dimensional, pixel-based error model, which characterizes …

K-means is one of the most simple and popular clustering algorithms, which implemented as
a standard clustering method in most of machine learning researches. The goal of K-means …

To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is
proposed. A core problem of the CASSI system is to recover the reliable and fine underlying …

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by
enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of …

Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has
received increasing attention in various practical applications. However, most traditional …

Non-negative matrix factorization (NMF) has become one of the most powerful methods for
clustering and feature selection. However, the performance of the traditional NMF method …

Spectral super-resolution, which reconstructs a hyperspectral image (HSI) from a single red-
green-blue (RGB) image, has acquired more and more attention. Recently, convolution …