Explore the vast range of titles available.

The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world’s seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especially in the rugged part of the terrain. The proposed method, which learns a dictionary to capture topographical features and reconstructs them using a dictionary, produces super-resolution with high interpretability.

Image fusion aims to integrate complementary information from different source images into the final output image. This plays a significant role in high-level vision tasks. However, image fusion methods based on sparse representation (SR) or conventional multiscale transform (MST) have some drawbacks that are difficult to overcome. As an alternative form of SR, convolutional sparse representation (CSR) has the advantages of detail preservation and shift-invariance, which can overcome the shortcomings of SR- and MST-based fusion methods. Since CSR has been widely used in the field of image fusion and has advanced this field to a great extent, it is necessary to conduct a comprehensive investigation of image fusion based on CSR. To the best of our knowledge, there are no previous papers reviewing and evaluating CSR-based fusion methods, and this study is the first retrospective. In this paper, we focus on CSR-based image fusion methods and review the recent advances in pixel-level image fusion based on CSR. In the experimental part of the paper, multifocal images, infrared-visible images, and multimodal medical images are used as test images to compare and evaluate the performance of different image fusion methods. In addition, the future trend of CSR-based image fusion is discussed. This paper is expected to serve as a resource of reference for both researchers and general learners seeking an overview of CSR-based image fusion.

As a branch of dictionary learning (DL), analysis dictionary learning has been widely used for pattern classification, which achieves outstanding performance. However, it is still a challenging to learn a more compact and discriminative analysis dictionary to ensure that the coding coefficient matrix of training samples presents a more discriminative block diagonal structure. To address this issue, we propose a self-eliminating discriminant analysis dictionary learning (SeDADL) method to learn a discriminant analysis dictionary that makes the coding coefficient matrix have an approximate block diagonal structure. Specifically, we first design a novel analysis dictionary regularization term to improve the discrimination capability of analysis dictionary by eliminating repeated and linearly dependent atoms in the analysis dictionary while preventing the generation of trivial solutions. Then, we design a self-eliminating coding coefficient constraint term to enhance the discrimination capability of spare codes by forcing the coding coefficient matrix to achieve an approximate block diagonal structure. In order to further improve the classification efficiency of SeDADL model, we introduce a linear classification error term into SeDADL model to learn a linear classifier, which constructs the links between spare codes and class labels. Moreover, an efficient iterative algorithm is designed to solve the optimization problem of SeDADL. Extensive experimental results on six datasets demonstrate that SeDADL can achieve satisfactory classification performance compared with some state-of-the-art methods.

We consider the problem of distributed dictionary learning which aims to learn a global dictionary from data geographically distributed on nodes of a network. Existing works are based on sparse synthesis model while this paper is based on sparse analysis model. Two novel distributed analysis dictionary learning (ADL) algorithms are proposed by adapting the centralized ADL algorithms Analysis SimCO (ASimCO) and Incoherent Analysis SimCO (INASimCO) to distributed settings. In particular, local representation vectors and local dictionaries are introduced, and they can be updated independently on each node by distributing the sparse coding and dictionary update stages of ASimCO. A diffusion strategy is then applied to estimate a global dictionary from the local dictionaries by exchanging local information. Experimental results with synthetic data and for image denoising demonstrate that the proposed distributed ADL algorithms can obtain similar results as correpsonding centralized algorithms.

In analysis dictionary learning (ADL) algorithms, the row vectors (profiles) of the analysis coefficient matrix and analysis atoms are always one-to-one correspondence, and the analysis information of atoms could be represented by their corresponding profiles. However, the analysis atoms and their corresponding profiles are seldom jointly explored to formulate a discrimination term. In this paper, we exploit the analysis atoms and profiles to design a structured discriminative ADL algorithm for image classification, called structured analysis dictionary learning based on discriminative Fisher pair (SADL-DFP). Specifically, we explicitly provide the definitions of the profile and the newly defined profile block, which are used to illustrate the analysis mechanism of the ADL model. Then, the discriminative Fisher pair (DFP) model is designed by using the Fisher criterion of analysis atoms and profiles, which can enhance the inter-class separability and intra-class compactness of the analysis atoms and profiles. Since the profiles and analysis atoms can be updated alternatively and interactively, our DFP model can further encourage the analysis atoms to analyze the same-class training samples as much as possible. In addition, a robust multiclass classifier is simultaneously learned by utilizing the label information of the training samples and analysis atoms in our SADL-DFP algorithm. The experimental results show that the proposed SADL-DFP algorithm can outperform many state-of-the-art dictionary learning algorithms on multiple datasets with both deep learning-based features and hand-crafted features.

Discriminative dictionary learning has been extensively used for pattern classification tasks. By incorporating different kinds of label information into the dictionary learning framework, a dictionary can be attained that represents the original signal with discriminative reconstruction. The previous works learn the dictionary in the original space which limits the dictionary learning performance. In this paper, we propose an approach, namely Deep Discriminative Dictionary Pair Learning (D 3 PL) for image classification. The input of D 3 PL is not the matrix collected by original gray images or hand-crafted features but the relatively deeper features derived from autoencoders. Then, a structured dictionary is designed based on the discriminative contributions across different classes to reconstruct the deep feature. In addition, the associated structured projective dictionary is learned as well to guarantee the decoders updating towards the minimal error of deconvolution operator. By leveraging the discriminative-dictionary-learning-based loss function and the autoencoder loss function, D 3 PL can simultaneously learn the deep potential feature and the corresponding dictionary pair. In the testing phase of D 3 PL, the minimum error between the deep feature and the structured projective component with regard to different classes can directly indicate the label by a basic matrix multiplication operation. Experimental results on challenging Extended Yale B, AR, UMIST, COIL20, Scene 15, and Caltech101 datasets demonstrate that the proposed D 3 PL outperforms the prominent dictionary learning methods.

The application of compressed sensing (CS) to biomedical imaging is sensational since it permits a rationally accurate reconstruction of images by exploiting the image sparsity. The quality of CS reconstruction methods largely depends on the use of various sparsifying transforms, such as wavelets, curvelets or total variation (TV), to recover MR images. As per recently developed mathematical concepts of CS, the biomedical images with sparse representation can be recovered from randomly undersampled data, provided that an appropriate nonlinear recovery method is used. Due to high under-sampling, the reconstructed images have noise like artifacts because of aliasing. Reconstruction of images from CS involves two steps, one for dictionary learning and the other for sparse coding. In this novel framework, we choose Simultaneous code word optimization (SimCO) patch-based dictionary learning that updates the atoms simultaneously, whereas Focal underdetermined system solver (FOCUSS) is used for sparse representation because of a soft constraint on sparsity of an image. Combining SimCO and FOCUSS, we propose a new scheme called SiFo. Our proposed alternating reconstruction scheme learns the dictionary, uses it to eliminate aliasing and noise in one stage, and afterwards restores and fills in the k-space data in the second stage. Experiments were performed using different sampling schemes with noisy and noiseless cases of both phantom and real brain images. Based on various performance parameters, it has been shown that our designed technique outperforms the conventional techniques, like K-SVD with OMP, used in dictionary learning based MRI (DLMRI) reconstruction.

Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products.

In the fields of computer vision and pattern recognition, dictionary learning techniques have been widely applied. In classification tasks, synthesis dictionary learning is usually time-consuming during the classification stage because of the sparse reconstruction procedure. Analysis dictionary learning, which is another research line, is more favorable due to its flexible representative ability and low classification complexity. In this paper, we propose a novel discriminative analysis dictionary learning method to enhance classification performance. Particularly, we incorporate a linear classifier and the supervised information into the traditional analysis dictionary learning framework by adding a discrimination error term. A synthesis K-SVD based algorithm which can effectively constrain the sparsity is presented to solve the proposed model. Extensive comparison experiments on benchmark databases validate the satisfactory performance of our method.

Existing discriminant analysis-synthesis dictionary pair learning (ASDPL) methods learn a structured analysis dictionary containing multiple class-specific analysis sub-dictionaries and a structured synthesis dictionary containing multiple class-specific synthesis sub-dictionaries. Although existing discriminant ASDPL methods achieve promising results in the field of pattern classification, most of them ignore the correlation between an analysis sub-dictionary and a synthesis sub-dictionary that belong to different classes, which may degrade the discriminative ability of their learned dictionary pairs. Moreover, most existing discriminant ASDPL methods do not give an explicit constraint to ensure that the reconstruction error of training samples under the joint action of a structured analysis dictionary and a structured synthesis dictionary is as small as possible, leading to insufficient representational ability of their learned dictionary pairs. To address these issues, we present a double-constrained structured discriminant analysis-synthesis dictionary pair learning (DCSDDPL) method. Specifically, we first design a class-specific analysis-synthesis sub-dictionary pair reconstruction constraint term to ensure that the reconstruction error of training samples of a class is as small as possible under the joint action of the analysis and synthesis sub-dictionaries belonging to the same class, which helps to improve the representational ability of the learned dictionary pair. Then, we design an analysis-synthesis sub-dictionary pair independence constraint term to eliminate the correlation between an analysis sub-dictionary and a synthesis sub-dictionary belonging to different classes so as to ensure the discriminative ability of the learned dictionary pair. Finally, we formulate the DCSDDPL model by integrating the two constraint terms into the basic discriminant analysis-synthesis dictionary pair learning model. Moreover, we design an optimization algorithm and use it to obtain the solution of the DCSDDPL model. We experimentally compare our method with five state-of-the-art dictionary learning methods, D-KSVD, LC-KSVD, FDDPL, DASDL and RA-DPL on the Extended Yale B, AR, PIE, CLD 22, Scene 15 and Caltech 101 datasets. The highest classification accuracy achieved by our method on these datasets is 99.13% and the highest F1-Score is 0.9906. The experimental results confirm that our method is effective for pattern classification.