Coupled Dictionary Learning Research Articles

Research on multi‐center assisted diagnosis of ASD based on multimodal feature fusion

AbstractAutism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) provide information about brain structure and function, aiding in objective ASD diagnosis. However, existing ASD classification methods face challenges such as sample scarcity, inter‐imaging center variations, insufficient single‐modality information, and inconsistent feature dimensions. This study introduced a method based on the Local Global Multimodal Domain Adaptation (LGMDA)‐Sparse Adaptive Prior Coupled Dictionary Learning (SACDL) framework. Initially, the LGMDA method was introduced to achieve multi‐source DA. By minimizing differences between different data domains while maximizing inter‐class differences within the same domain, it expands the sample size of multi‐modal data, addressing the issues of sample scarcity and heterogeneity in ASD data. Subsequently, the SACDL method was employed for multimodal fusion. It initialized dictionaries using the ATGP algorithm, combined sMRI and fMRI data for dictionary learning, adaptively adjusted sparsity parameters, and integrated ASD phenotype data for constrained optimization. It enables joint learning of shared and modality‐specific features, balancing differences in feature dimensions. Experimental results show that this model effectively utilizes multi‐center, multi‐modal information to achieve better auxiliary diagnosis than single‐modal small samples. This method has the potential to provide effective solutions for ASD multi‐source and multi‐modal classification problems, which are significant for ASD research and clinical diagnosis.

CROSS STYLE IMAGE SYNTHESIS THROUGH SEMI COUPLED DICTIONARY LEARNING AND GENERALIZED CROSS VALIDATION ALGORITHM

In various Computer vision applications for better visualization, interpretation and better recognition, we frequently want to change an image in one style into another one which is a large research area of compressive sensing. Algorithms on compressed sensing are in demand to create better reconstruction of the images while taking less computational time and requiring less storage capacity. In the present work attempts are put in the same direction and brought an algorithm which includes semi coupled dictionary learning (SCDL) model of compressed sensing with Singular Value Decomposition (SVD) based Generalized Cross Validation (GCV) algorithm. Using this algorithm one can find the different value of regularization parameters for different sizes of images and that helps to get better reconstruction of the images with ease while doing experiments on cross style image synthesis problems.

Joint Sparse Representations and Coupled Dictionary Learning in Multisource Heterogeneous Image Pseudo-Color Fusion

Joint Sparse Representations and Coupled Dictionary Learning in Multisource Heterogeneous Image Pseudo-Color Fusion

Robotic Object Perception Based on Multispectral Few-Shot Coupled Learning

In order to enable intelligent robots to recognize unknown objects as accurately as human beings, object perception research is of great significance in service and industrial robot application scenarios. However, object perception using spectral measurements under few-shot learning usually leads to a poor result because of inadequate training samples. To overcome this problem, this work proposes a novel few-shot learning with coupled dictionary learning (FSL-CDL) framework. First, a hybrid feature fusion method is developed to extract the multiple dimension-reduced features of original spectral measurements to build the hybrid features. Then, based on the hybrid features, a multitask coupled learning method is developed to effectively recognize unknown objects under few-shot learning. In this method, two coupling patterns, i.e., interspectroscopy coupling and intraspectroscopy coupling, effectively bridge the gap between two spectral measurements. Finally, the proposed FSL-CDL is compared with other advanced algorithms on the SMM50 dataset, and reaches 97.5% and 98.4% recognition accuracy under one-shot and five-shot learning, respectively, which are better than other algorithms. Besides, FSL-CDL can be extended to other perception tasks which contains multiple heterogeneous measurements.

Joint coupled dictionaries-based visible-infrared image fusion method via texture preservation structure in sparse domain

Joint sparse coding (JSC) and coupled dictionary learning (CDL) have been successful in visible and infrared image fusion tasks. However, JSC fusion methods using a single dictionary cannot perfectly characterize different modal signals. The CDL-based fusion methods ignore the role of high-frequency components in texture preservation and sharpness. In this study, we design fusion rules while considering the uniqueness and connection of different feature spaces to alleviate this issue. In the proposed fusion method, coupled dictionaries are first used to establish the relationship between infrared and visible images. Then, we use joint sparse coding and coupled dictionaries to calculate sparse coefficients via the joint sparse model. Finally, the improved coefficient fusion strategy realizes the conversion and synthesis between different spatial images. Experimental results on the TNO and RoadScene datasets show that the proposed method achieves promising results in terms of objective and subjective performance compared with state-of-the-art algorithms.

Joint sparse model with coupled dictionary for medical image fusion

Medical image fusion has made great progress benefit from strong feature association ability of joint sparse model. Since medical images are obtained by different sensors with different imaging mechanisms, single dictionary cannot effectively characterize innovation and common information, which becomes stumbling block for effective fusion. In this paper, we propose novel joint sparse model with coupled dictionary learning for medical image fusion. Our framework design new fusion rule to enhance ability of multi-source signal preservation and retain edge/texture information. Firstly, source medical images was represented as a common sparse component and innovation sparse components with over-complete coupled dictionaries. Secondly, fused sparse coefficient is obtained by using the designed novel rule. Finally, fused results are reconstructed using fused coefficients and coupled dictionaries. Our proposed method is evaluated and compared with 9 state-of-the-art fusion methods on public Harvard dataset. Qualitative and quantitative results show that our method is generally better than the compared methods in terms of retaining source image structural and functional information.

Multimodal image fusion via coupled feature learning

This paper presents a multimodal image fusion method using a novel decomposition model based on coupled dictionary learning. The proposed method is general and can be used for a variety of imaging modalities. In particular, the images to be fused are decomposed into correlated and uncorrelated components using sparse representations with identical supports and a Pearson correlation constraint, respectively. The resulting optimization problem is solved by an alternating minimization algorithm. Contrary to other learning-based fusion methods, the proposed approach does not require any training data, and the correlated features are extracted online from the data itself. By preserving the uncorrelated components in the fused images, the proposed fusion method significantly improves on current fusion approaches in terms of maintaining the texture details and modality-specific information. The maximum-absolute-value rule is used for the fusion of correlated components only. This leads to an enhanced contrast-resolution without causing intensity attenuation or loss of important information. Experimental results show that the proposed method achieves superior performance in terms of both visual and objective evaluations compared to state-of-the-art image fusion methods.

Class-guided coupled dictionary learning for multispectral-hyperspectral remote sensing image collaborative classification

Class-guided coupled dictionary learning for multispectral-hyperspectral remote sensing image collaborative classification

Zero-shot image classification using coupled dictionary embedding

Zero-shot learning (ZSL) is a framework to classify images that belong to unseen visual classes using their semantic descriptions about the unseen classes. We develop a new ZSL algorithm based on coupled dictionary learning. The core idea is to enforce the visual features and the semantic attributes of an image to share the same sparse representation in an intermediate embedding space, modeled as the shared input space of two sparsifying dictionaries. In the ZSL training stage, we use images from a number of seen classes for which we have access to both the visual and the semantic attributes to train two coupled dictionaries that can represent both the visual and the semantic feature vectors of an image using a single sparse vector. In the ZSL testing stage and in the absence of labeled data, images from unseen classes are mapped into the attribute space by finding the joint-sparse representations using solely the visual dictionary via solving a LASSO problem. The image is then classified in the attribute space given semantic descriptions of unseen classes. We also provide attribute-aware and transductive formulations to tackle the “domain-shift” and the “hubness” challenges for ZSL, respectively. Experiments on four primary datasets using VGG19 and GoogleNet visual features, are provided. Our performances using VGG19 features are 91.0%, 48.4%, and 89.3% on the SUN, the CUB, and the AwA1 datasets, respectively. Our performances on the SUN, the CUB, and the AwA2 datasets are 57.0%,49.7%, and 71.7%, respectively, when GoogleNet features are used. Comparison with existing methods demonstrates that our method is effective and compares favorably against the state-of-the-art. In particular, our algorithm leads to decent performance on the all four datasets.22Early partial results of this paper is presented at 2018 AAAI (Kolouri, Rostami, Owechko, & Kim, 2018).

A Change Detection Method for Remote Sensing Images Based on Coupled Dictionary and Deep Learning.

With the rapid development of remote sensing technology, change detection (CD) methods based on remote sensing images have been widely used in land resource planning, disaster monitoring, and urban expansion, among other fields. The purpose of CD is to accurately identify changes on the Earth's surface. However, most CD methods focus on changes between the pixels of multitemporal remote sensing image pairs while ignoring the coupled relationships between them. This often leads to uncertainty about edge pixels with regard to changing objects and misclassification of small objects. To solve these problems, we propose a CD method for remote sensing images that uses a coupled dictionary and deep learning. The proposed method realizes the spatial-temporal modeling and correlation of multitemporal remote sensing images through a coupled dictionary learning module and ensures the transferability of reconstruction coefficients between multisource image blocks. In addition, we constructed a differential feature discriminant network to calculate the dissimilarity probability for the change area. A new loss function that considers true/false discrimination loss, classification loss, and cross-entropy loss is proposed. The most discriminating features can be extracted and used for CD. The performance of the proposed method was verified on two well-known CD datasets. Extensive experimental results show that the proposed method is superior to other methods in terms of precision, recall, F1-score, IoU, and OA.

Separable Coupled Dictionary Learning for Large-Scene Precise Classification of Multispectral Images

Large-scene precise classification of multispectral images (MSIs) has become one of the hot topics in remote sensing field. MSIs usually have wide swath and a meter or even submeter level of spatial resolution, which make large-scene observation possible. However, the limited number of spectral bands leads to the confusion of land covers in classification, especially for the large-scene conditions with abundant land cover types. Therefore, overlapped hyperspectral images (HSIs) can be used to improve the precision degree of classification. To achieve this purpose, coupled dictionary learning has been proposed as a major means. Aiming at separating the class-specific characteristics and mutual patterns among different land covers, this paper proposed a separable coupled dictionary learning (SCDL) method, which converts the separation of mutual features into the construction of separable coupled dictionaries and learns both class-specific coupled dictionaries and mutual coupled dictionaries simultaneously with the aid of label information. More specifically, the proposed method uses the labels of training samples to construct class-specific reconstruction error constraint, class-specificity constraint and separable dictionary incoherence constraint as regularization terms, to make sure that the learned coupled dictionaries to be both compact and discriminative. The learned separable coupled dictionaries facilitate pixels belong to the same category to be represented by the mutual dictionary and the class-specific sub-dictionary of corresponding class. The experiments compared with several state-of-the-art methods on three pairs of HSI and MSI have shown better classification performance.

Integrating Coupled Dictionary Learning and Distance Preserved Probability Distribution Adaptation for Multispectral–Hyperspectral Image Collaborative Classification

With the development of observation technology in remote sensing (RS), large-area multispectral (MS) images can be easily obtained. However, due to the limitation of imaging devices, only a limited range of hyperspectral (HS) images with higher spectral resolution can be obtained. This article mainly focuses on how to use limited HS images to improve the classification performance of MS images. In order to solve this problem, this article proposes an MS–HS image collaborative classification method, which integrates coupled dictionary learning and distance preserved probability distribution. First, image reconstruction based on coupled dictionary learning is performed, in which sparse representation and dictionary learning are used to generate HS images from MS images through spectral superresolution, so that the spectral features of the MS data and HS data are converted to the same feature space for feature space alignment. Second, the probability distribution is adapted, in which the marginal and conditional probabilities are adapted to further narrow the difference between the real HS data and the generated HS data. At the same time, the consistency of the data structure of the source domain before and after the mapping is maintained, so that the same class of data is more compact after the mapping and reduces the spacing within the same class. Compared with the state-of-the-art methods, this article conducts the experiments on three MS–HS RS datasets, which demonstrate the superiority of the proposed method.

Low-dose PET image denoising based on coupled dictionary learning

Reducing the doses of tracers in positron emission tomography (PET) scans can effectively reduce the damage caused to the patient’s health, but at the same time, doing so reduces the quality of the resulting image. The purpose of this study is to reduce the noise in low-dose (LD) PET images through a coupled dictionary learning method and improve the feature similarity between LD images and standard-dose images. The main method in this paper involves training a coupled dictionary with LD and standard-dose PET image samples and then utilizing the updated dictionary to reconstruct a denoised image. This article changes the dictionary training process by not directly updating the joint dictionary, but rather updating the two dictionaries separately in a specific manner. We compare a variety of traditional denoising algorithms that are applied to LD PET images. The proposed method significantly improves the quality of the resulting image, effectively removing the noise from the image and better retaining the image details. In addition, the reconstructed image is closest to the standard-dose PET image.

Super-Energy-Resolution Material Decomposition for Spectral Photon-Counting CT Using Pixel-Wise Learning

Spectral photon-counting CT offers novel potentialities to achieve quantitative decomposition of material components, in comparison with traditional energy-integrating CT or dual-energy CT. Nonetheless, achieving accurate material decomposition, especially for low-concentration materials, is still extremely challenging for current sCT, due to restricted energy resolution stemming from the trade-off between the number of energy bins and undesired factors such as quantum noise.We propose to improve material decomposition by introducing the notion of super-energy-resolution in sCT. The super-energy-resolution material decomposition consists in learning the relationship between simulation and physical phantoms in image domain. To this end, a coupled dictionary learning method is utilized to learn such relationship in a pixel-wise way. The results on both physical phantoms and in vivo data showed that for the same decomposition method using lasso regularization, the proposed super-energy-resolution method achieves much higher decomposition accuracy and detection ability in contrast to traditional image-domain decomposition method using L1-norm regularization.

Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

It is very interesting to reconstruct high-resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super-resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K-singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low-resolution images, the authors prepare dictionaries over LR and HR image patches using the KSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal-to-noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high-resolution image. These parameters play an essential role in the reconstruction of the HR images.

Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong learning method based on coupled dictionary learning that utilizes high-level task descriptions to model inter-task relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of learning problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict a model for the new task through zero-shot learning using the coupled dictionary, eliminating the need to gather training data before addressing the task.

Single Image Super-Resolution through Sparse Representation via Coupled Dictionary learning

Single Image Super-Resolution through Sparse Representation via Coupled Dictionary learning

An Efficient Coupled Dictionary Learning Method

In this letter, we propose a novel computationally efficient coupled dictionary learning method that enforces pairwise correlation between the atoms of dictionaries learned to represent the underlying feature spaces of two different representations of the same signals, e.g., representations in different modalities or representations of the same signals measured with different qualities. The jointly learned correlated feature spaces represented by coupled dictionaries are used in sparse representation based classification, recognition and reconstruction tasks. The presented experimental results show that the proposed coupled dictionary learning method has a significantly lower computational cost. Moreover, the visual presentation of jointly learned dictionaries shows that the pairwise correlations between the corresponding atoms are ensured.

Coupled dictionary learning for unsupervised change detection between multimodal remote sensing images

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through sensors of different modalities. This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by sensors of different modalities with possibly different resolutions. These sensor dissimilarities introduce additional issues in the context of operational change detection that are not addressed by most of the classical methods. This paper introduces a novel framework to effectively exploit the available information by modeling the two observed images as a sparse linear combination of atoms belonging to a pair of coupled overcomplete dictionaries learnt from each observed image. As they cover the same geographical location, codes are expected to be globally similar, except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a dual code estimation which enforces spatial sparsity in the difference between the estimated codes associated with each image. This problem is formulated as an inverse problem which is iteratively solved using an efficient proximal alternating minimization algorithm accounting for nonsmooth and nonconvex functions. The proposed method is applied to real images with simulated yet realistic and real changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.

Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction.

Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a coupled dictionary learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled k -space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing k -space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the k -space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variety of sampling schemes, demonstrate that CDLMRI is capable of capturing structural dependencies between different contrasts. The learned priors indicate notable advantages in multi-contrast MR imaging and promising applications in quantitative MR imaging such as MR fingerprinting.