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Deep generalized unfolding networks for image restoration
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 …
most DNN methods are designed as a black box, lacking transparency and interpretability …
Deep convolutional dictionary learning for image denoising
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 …
have been proposed to integrate traditional image modeling techniques, such as dictionary …
Model-driven deep unrolling: Towards interpretable deep learning against noise attacks for intelligent fault diagnosis
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 …
to deep learning (DL)-based methods over the decades. However, the “black box” nature of …
An effective and efficient algorithm for K-means clustering with new formulation
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 …
a standard clustering method in most of machine learning researches. The goal of K-means …
Residual degradation learning unfolding framework with mixing priors across spectral and spatial for compressive spectral imaging
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 …
proposed. A core problem of the CASSI system is to recover the reliable and fine underlying …
Spectral super-resolution via model-guided cross-fusion network
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 …
green-blue (RGB) image, has acquired more and more attention. Recently, convolution …
Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes
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 …
regression methods use the one-dimensional, pixel-based error model, which characterizes …
Memory-augmented deep unfolding network for guided image super-resolution
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 …
enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of …
A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning
Q Ma, H Li - Expert Systems with Applications, 2024 - Elsevier
Evaluating suppliers' quality performance is one of critical tasks of quality management
since it is directly related to quality assurance, improvement and development, especially for …
since it is directly related to quality assurance, improvement and development, especially for …
[HTML][HTML] Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods
One of the essential problems in multi-criteria decision-making (MCDM) is ranking a set of
alternatives based on a set of criteria. In this regard, there exist several MCDM methods …
alternatives based on a set of criteria. In this regard, there exist several MCDM methods …