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In this paper, we propose a novel face recognition method that embeds the locality-constrained sparse representation in the dictionary learning framework. The shared-specific dictionary learning is employed to explicitly learn class-specific dictionary for each class that captures the most discriminative features of this class, and simultaneously learn a shared dictionary, whose atoms are shared by...
In this paper, we propose a blind motion deblurring method based on sparse representation and structural self-similarity from a single image. The priors for sparse representation and structural self-similarity are explicitly added into the recovery of the latent image by means of sparse and multi-scale nonlocal regularizations, and the down-sampled version of the observed blurry image is used as training...
Recent learning-based face super-resolution methods, such as Yang's Sparse Coding Super-Resolution (SCSR) are promising with sharp edges visually. But it also leads to obvious artifacts. In order to eliminate the artifacts, Online Dictionary Learning (ODL) algorithm is introduced in the dictionary learning phase to generate accurate overcomplete dictionary. On the other hand, the reconstruction regularization...
This paper introduces a segmentation approach, where a discriminative dictionary with objects' shape information is learned, followed by a sparse representation based segmentation process. In contrast with state-of-the-art sparse representation classification methods using discriminative dictionary learning, the proposed method learns a discriminative dictionary containing both intensity and shape...
Resolution in medical images is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. In this paper, we propose a coupled dictionary learning approach for super...
In this paper, we present a novel classification model which combines the convolutional sparse coding framework with the classification strategy. In the training phase, the proposed model trained a convolutional filter bank by all images of each class. In the test phase, the label of test image is determined by all convolutional filter banks. Compared with canonical sparse representation and dictionary...
Fisher discrimination dictionary sparse learning (FDDL) has led to interesting image recognition results where the Fisher discrimination criterion is subject to the coding coefficients. But Fisher discrimination criterion has the limitations of data distribution assumptions and does not consider the local manifold structure of the coding coefficients. In this paper, we will introduce a novel Fisher...
In this paper, super-resolution image is obtained from a single low-resolution image using dictionary learning approach. The original image is blurred and downsampled to the low-resolution image, and has to find the value which is lost during downsampling and trained with patches. Each patches of low-resolution image use that value of their respective high-resolution image during training of dictionary...
Sparse prior provides an effective tool for the image reconstruction. However, the sparse coding for independent patches leads to the unstable sparse decomposition. In this paper, we propose a group structured sparse representation model by considering the nonlocal similarity. The nonlocal similar patches are collected and classified into groups. Patches in the same group are reconstructed based the...
In this paper, we present an automated system for robust biometric recognition based upon sparse representation and dictionary learning. In sparse representation, extracted features from the training data are used to develop a dictionary. Training data of real world applications are likely to be exposed to geometric transformations, which is a big challenge for designing of discriminative dictionaries...
Recent years has witnessed an increasing interest in handling the issue of single image super-resolution (SISR) reconstruction. Many researches have demonstrated that the sparse representation based approaches, which rely on the ideal that image patches are assumed to have brief representations when expressed in the proper learned dictionaries, can lead to the state-of-the-art performance. The SISR...
This paper presents a new approach to single-image super-resolution reconstruction, based on sparse signal representation using classified dictionaries. The high-resolution and low-resolution image patches training sets are divided into two categories respectively by two new classification templates which give consideration to direction and edge features. Then, we train a pair of learning dictionaries...
Due to the limitation of hardware, Infrared (IR) image has low-resolution (LR) and poor visual quality. Infrared image super-resolution (SR) is a good solution for this problem. However, the conventional SR methods have some drawbacks. Firstly, the trained dictionary is an unstructured dictionary, which may lead to worse results. Secondly, the representation of the image is too simple to effectively...
Image super-resolution with sparsity prior provides promising performance. However, traditional sparse-based super resolution methods transform a two dimensional (2D) image into a one dimensional (1D) vector, which ignores the intrinsic 2D structure as well as spatial correlation inherent in images. In this paper, we propose the first image super-resolution method which reconstructs a high resolution...
In dictionary-learning-based face hallucination, the testing image is represented as a linear combination of the training samples, and how to obtain the optimal coefficients is the primary issue. Sparse representation (SR) has ever been widely used in face hallucination, however, due to the fact that SR overemphasizes the sparsity, the obtained linear combination coefficients turn out far aggressively...
In recent years, with the theory of compressed sensing being proposed and applied widely, the sparse representation method has become one of the hotspots to handle the superresolution problem. Usually, this kind of algorithms use only one dictionary pair for all low-resolution patches, which makes the recovered results less satisfied due to its bad adaptability. To overcome such problem, in this paper,...
In this paper a new algorithm for single-image super-resolution based on sparse representation over a set of coupled low and high resolution dictionary pairs is proposed. The sharpness measure is defined via the magnitude of the gradient operator and is shown to be approximately scale-invariant for low and high resolution patch pairs. It is employed for clustering low and high resolution patches in...
We propose a single-image super-resolution algorithm based on sparse representation over a set of cluster dictionary pairs. For each cluster, a directionally structured dictionary pair is designed. The dominant angle in the patch gradient phase matrix is employed as an approximately scale-invariant measure. This measure serves for patch clustering and sparse model selection. The dominant phase angle...
In this paper we consider the problem of the pose variance in face recognition. In practice, the face images we get usually have different poses; however the number of training sample is limited, so the sparse representation based classification (SRC) for face recognition cannot work well. For the purpose of expending the sample size, a 3D face modal based image synthesis method is adopted. Experiments...
Based on the idea of SRC(Sparse Representation based Classification), a novel approach HSIC-SRC is proposed in this paper. Unlike most existing algorithms for HSIC(HSI Classification) via sparse representation, our main contributions lie in two aspects, 1) Considering the performance of SRC depending on the quality of dictionary, we employ LC-KSVD(Label Consistent KSVD) algorithm which joints the...
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