K-SVD
View article
Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation Open
This paper presents a variational based approach to fusing hyperspectral and\nmultispectral images. The fusion process is formulated as an inverse problem\nwhose solution is the target image assumed to live in a much lower dimensional\nsub…
View article
Efficient Algorithms for Convolutional Sparse Representations Open
When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but result…
View article
Convolutional Dictionary Learning: A Comparative Review and New Algorithms Open
Convolutional sparse representations are a form of sparse representation with a dictionary that has a structure that is equivalent to convolution with a set of linear filters. Additionally, while effective algorithms have recently been dev…
View article
A Survey of Dictionary Learning Algorithms for Face Recognition Open
During the past several years, as one of the most successful applications of sparse coding and dictionary learning, dictionary-based face recognition has received significant attention. Although some surveys of sparse coding and dictionary…
View article
Trainlets: Dictionary Learning in High Dimensions Open
Sparse representations has shown to be a very powerful model for real world\nsignals, and has enabled the development of applications with notable\nperformance. Combined with the ability to learn a dictionary from signal\nexamples, sparsit…
View article
When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition With Limited Data Open
We present a new deep dictionary learning and coding network (DDLCN) for image-recognition tasks with limited data. The proposed DDLCN has most of the standard deep learning layers (e.g., input/output, pooling, and fully connected), but th…
View article
Sparse and Spurious: Dictionary Learning With Noise and Outliers Open
A popular approach within the signal processing and machine learning communities consists in mod-elling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical su…
View article
A Novel Geometric Dictionary Construction Approach for Sparse Representation Based Image Fusion Open
Sparse-representation based approaches have been integrated into image fusion methods in the past few years and show great performance in image fusion. Training an informative and compact dictionary is a key step for a sparsity-based image…
View article
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization Open
We consider the problem of sparse coding, where each sample consists of a sparse linear combination of a set of dictionary atoms, and the task is to learn both the dictionary elements and the mixing coefficients. Alternating minimization i…
View article
A Multidimensional Data‐Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition Open
Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This we…
View article
Linearized Kernel Dictionary Learning Open
In this paper we present a new approach of incorporating kernels into\ndictionary learning. The kernel K-SVD algorithm (KKSVD), which has been\nintroduced recently, shows an improvement in classification performance, with\nrelation to its …
View article
Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging Open
Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery fromundersampledmeasurements…
View article
Coupled Dictionary Learning for Unsupervised Feature Selection Open
Unsupervised feature selection (UFS) aims to reduce the time complexity and storage burden, as well as improve the generalization performance. Most existing methods convert UFS to supervised learning problem by generating labels with speci…
View article
Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation Open
Most hyperspectral anomaly detection methods directly utilize all the original spectra to recognize anomalies. However, the inherent characteristics of high spectral dimension and complex spectral correlation commonly make their detection …
View article
SPORCO: A Python package for standard and convolutional sparse representations Open
SParse Optimization Research COde (SPORCO) is an open-source Python package for solving optimization problems with sparsity-inducing regularization, consisting primarily of sparse coding and dictionary learning, for both standard and convo…
View article
Sparse Deep Stacking Network for Image Classification Open
Sparse coding can learn good robust representation to noise and model more higher-order representation for image classification. However, the inference algorithm is computationally expensive even though the supervised signals are used to l…
View article
Image‐denoising algorithm based on improved K‐singular value decomposition and atom optimization Open
The traditional K‐singular value decomposition (K‐SVD) algorithm has poor image‐denoising performance under strong noise. An image‐denoising algorithm is proposed based on improved K‐SVD and dictionary atom optimization. First, a correlati…
View article
Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning Open
In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the spars…
View article
A novel sparse feature extraction method based on sparse signal via dual-channel self-adaptive TQWT Open
Sparse signal is a kind of sparse matrices which can carry fault information and simplify the signal at the same time. This can effectively reduce the cost of signal storage, improve the efficiency of data transmission, and ultimately save…
View article
Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning Open
Despite the rapid developments of X-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this paper, a sparse…
View article
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems Open
The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared …
View article
Joint sparse model-based discriminative K-SVD for hyperspectral image classification Open
Sparse representation classification (SRC) is being widely investigated on hyperspectral images (HSI). For SRC methods to achieve high classification performance, not only is the development of sparse representation models essential, the d…
View article
Single Image Super-Resolution Based on Deep Learning Features and Dictionary Model Open
In traditional single image super-resolution (SR) methods based on dictionary model, a large number of image features are needed to train the SR dictionary. In general, these features are extracted by artificial rules, such as pixel gray, …
View article
First- and Second-Order Methods for Online Convolutional Dictionary Learning Open
Convolutional sparse representations are a form of sparse representation with a structured, translation-invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to…
View article
Supervised Dictionary Learning and Sparse Representation-A Review Open
Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer…
View article
Adaptive Iterated Shrinkage Thresholding-Based Lp-Norm Sparse Representation for Hyperspectral Imagery Target Detection Open
In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse…
View article
Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition Open
Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is a…
View article
Image Denoising via Sparse Representation Over Grouped Dictionaries With Adaptive Atom Size Open
The classic K-SVD based sparse representation denoising algorithm trains the dictionary only with one fixed atom size for the whole image, which is limited in accurately describing the image. To overcome this shortcoming, this paper presen…
View article
Analysis-Synthesis Dictionary Learning for Universality-Particularity Representation Based Classification Open
Dictionary learning has played an important role in the success of sparse representation. Although synthesis dictionary learning for sparse representation has been well studied for universality representation (i.e., the dictionary is unive…
View article
Image fusion via nonlocal sparse K-SVD dictionary learning Open
Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the repres…