A super-resolution (SR) method based on compressive sensing (CS), structural self-similarity (SSSIM), and dictionary learning is proposed for reconstructing remote images. This aims to identify a that represents high resolution (HR) image patches in sparse manner. Extra information from similar structures which often exist images can be introduced into the dictionary, thereby enabling an HR reconstructed using CS framework. We use K-Singular Value Decomposition obtain orthogonal matching...

Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided suggestions, or increase accuracy when lack of experts. The core problem this issue how to capture appropriate features specific task. Here, we propose feature extraction based on convolution neural networks (CNNs), try introduce more meaningful semantic classification. Firstly, CNN model trained with massive natural dataset...

Imaging in poor weather is often severely degraded by scattering due to suspended particles the atmosphere such as haze, fog and mist. Poor visibility becomes a major problem for most outdoor vision applications. In this paper, we propose novel fast defogging method from single image of scene based on bilateral filtering approach. The complexity our only linear function number input pixels thus allows very implementation. Results variety foggy images demonstrate that achieves good...

Text clustering is one of the central problems in text mining and information retrieval area. For high dimensionality feature space inherent data sparsity, performance algorithms will dramatically decline. Two techniques are used to deal with this problem: extraction selection. Feature selection methods have been successfully applied categorization but seldom due unavailability class label information. In paper, four unsupervised methods, DF, TC, TVQ, a new proposed method TV introduced....

Hyperspectral images (HSIs) have been used in a wide range of fields, such as agriculture, food safety, mineralogy, and environment monitoring, but being corrupted by various kinds noise limits its efficacy. Low-rank representation (LRR) has proved effectiveness the denoising HSIs. However, it just employs local information for denoising, which results ineffectiveness when is heavy. In this paper, we propose an approach group low-rank (GLRR) HSI denoising. our GLRR, divided into overlapping...

With the advantage of quick response and flexible ramp, energy storage system (ESS) offers a promising capability fast frequency control for power systems, especially under severe disturbance. This paper proposes an ESS strategy using local measurement in order to provide support right after sufficiently disturbance is observed. The proposed includes online characteristic estimator optimization controller. Distinguished from existing approaches, no prior knowledge model required this work,...

Spectral or spatial dictionary has been widely used in fusing low-spatial-resolution hyperspectral (LH) images and high-spatial-resolution multispectral (HM) images. However, only using spectral is insufficient for preserving information, vice versa. To address this problem, a new LH HM image fusion method termed OTD optimized twin dictionaries proposed paper. The problem of formulated analytically the framework sparse representation, as an optimization spectral-spatial their corresponding...

In this paper, a novel recurrent sigma‒sigma neural network (RSPSNN) that contains the same advantages as higher-order and networks is proposed. The batch gradient algorithm used to train RSPSNN search for optimal weights based on minimal mean squared error (MSE). To substantiate unique equilibrium state of RSPSNN, characteristic stability convergence proven, which one most significant indices reflecting effectiveness overcoming instability problem in training network. Finally, establish...

Estimating similarity between vertices is a fundamental issue in network analysis across various domains, such as social networks and biological networks. Methods based on common neighbors structural contexts have received much attention. However, both categories of methods are difficult to scale up handle large (with billions nodes). In this paper, we propose sampling method that provably accurately estimates the vertices. The algorithm novel idea random path. Specifically, given network,...

Based on the spatial dependence assumption, super-resolution mapping can predict location of land cover classes within mixed pixels. In this paper, we propose a novel method via multi-dictionary based sparse representation, which is robust to noise in both learning and class allocation process. To better distinguish different classes, distribution modes are learned separately. A spectral distortion constraint introduced, combining with reconstruction errors as metrics perform classification....

Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution (LHS) and multispectral (HMS) is usually formulated as a spatial super-resolution problem LHS with help an HMS image, that may result in loss detailed structural information. Facing above problem, fusion nonlinear spectral mapping from to HHS novel cluster-based method multi-branch BP neural networks (named CF-BPNNs) proposed, ensure more reasonable for each cluster. In training stage, considering...

In recent years, deep learning has been widely applied to hyperspectral image (HSI) classification with great success. However, since labeling images is time-consuming and labor-intensive, a limited number of labeled are available, making it difficult train feature extractors classifiers. To address this challenge, paper introduces self-supervised spectral-spatial graph prototypical network for few-shot HSI (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Imaging in poor weather is often severely degraded by scattering due to suspended particles the atmosphere such as haze and fog. In this paper, we propose a novel fast defogging method from single image of scene based on atmospheric model. inference process veil, coarser estimate refined using edge-preserving smoothing approach. The complexity proposed only linear function number pixels thus allows very implementation. Results variety outdoor foggy images demonstrate that achieves good...

Atmospheric conditions induced by suspended particles, such as fog and haze, severely alter the scene appearance. In this paper, we propose a novel defogging method based on local extrema, aiming at improving image visibility under foggy or hazy weather condition. The proposed utilizes atmospheric scattering model to realize removal. It applies extrema figure out three pyramid levels estimate veil, manipulates tone contrast of details different scales through multi-scale manipulation...

Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information. However, CNN can only perform convolution calculations on Euclidean datasets. To solve this problem, recently, graph convolutional network (GCN) is proposed, which be applied semisupervised HSI problem. GCN a direct push learning method, requires all nodes participate in training process get node embedding....

Hyperspectral unmixing is an important technique for remote sensing image analysis. Among various techniques, nonnegative matrix factorization (NMF) shows unique advantage in providing a unified solution with well physical interpretation. In order to explore the geometric information of hyperspectral data, graph regularization often used improve NMF performance. It groups neighboring pixels, uses as vertices, and then assigns weights connected vertices. The construction neighborhood are...