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The deep learning neural network is a recent development that has become the subject of research in the computer vision and remote sensing disciplines. Super resolution (SR) images can be obtained using deep neural network methods that achieve a higher performance than all previous traditional methods. Here, in this study, the objective is to describe existing deep learning methods for SR satellite...
Compressed sensing (CS) has drawn many interest in the field ultrasound (US) image recovery. It has demonstrated promising results in the recovery of radio-frequency element raw-data [Liebgott et. al. ULTRAS13, Besson et. al. SPARS17]. The objective of such approaches is to recover the raw-data from undersampled random measurements. It is achieved by means of convex optimization or greedy methods...
Recent research in computed tomographic imaging has focused on developing techniques that enable reduction of the X-ray radiation dose without loss of quality of the reconstructed images or volumes. While penalized weighted-least squares (PWLS) approaches have been popular for CT image reconstruction, their performance degrades for very low dose levels due to the inaccuracy of the underlying WLS statistical...
In this paper, a method for reducing coding artifacts introduced by lossy image compression is proposed. The method is similar to sample adaptive offset (SAO) which is adopted in the H.265/HEVC video coding standard as one of in-loop filtering tools. In the SAO, samples of the reconstructed image are classified into several categories based on some simple algorithms, and an optimum offset value is...
Orientation Field (OF) is one of the most significant characters to distinguish fingerprint images from non-fingerprint images. An effective definition of fingerprint OF pattern will not only benefit fingerprint enhancement, but also contribute to latent fingerprint detection and segmentation. The existing fingerprint OF models either require pre-knowledge of singular points, or cannot be generalized...
Vehicle verification in two different views can be applied for Intelligent Transportation System. However, object appearance matching in two different views is difficult. The vehicle images captured in two views are represented as a feature pair which can be classified as the same/different pair. Sparse representation (SR) has been applied for reconstruction, recognition, and verification. However,...
The coefficient of high resolution (HR) block and low resolution (LR) block is assumed to be equal in selecting the corresponding atoms of HR dictionary in previous super-resolution (SR) reconstruction algorithms, which may cause error matching and decrease the accuracy of HR coefficient estimation. A learning method of structural dictionary and mapping relation (LCDMR) is combined to compensate this...
The kernel trick becomes a burden for some machine learning tasks such as dictionary learning, where a huge amount of training samples are needed, making the kernel matrix gigantic and infeasible to store or process. In this work, we propose to alleviate this problem and achieve Gaussian RBF kernel expansion explicitly for dictionary learning using Fastfood transform, which is an approximation of...
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 ear recognition problems, sparse representation based classification (SRC) has shown good performance. The dictionary used for sparse coding plays a key role in SRC. Traditional SRC methods mostly use the holistic features of the training samples to construct the dictionary for identification. But this will bring heavy computational load because of the large dimensionality of the dictionary. Therefore,...
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 order to distinguish cover images and stego images, JPEG steganalysis technology has growing ties with machine learning in recent years. As an important research field in machine learning, dictionary learning (DL) has been successfully applied to various tasks, but its application in steganalysis is insufficient. In this paper, we propose a hybrid dictionary learning framework for JPEG steganalysis...
The development of accurate binocular vision relies on the acquisition of disparity tuning and the calibration of vergence eye movements. Both processes are fundamentally limited by visual acuity, which increases only gradually during the first year of life. Next to limiting performance, however, early limitations of visual acuity may also aid rapid learning analogous to Newport's “less-is-more” hypothesis...
This paper explores the enhancement by locality constraint to both learning and coding schemes, more specifically, discriminative low-rank dictionary learning and auto-encoder. Previous Fisher discriminative based dictionary learning has led to interesting results by learning more discerning sub-dictionaries. Also, the low-rank regularization term has been introduced to take advantage of the global...
Recently, the sparse coding based image representation has achieved state-of-the-art recognition results on many benchmarks. In this paper, we propose Multi-cue Normalized Non-Negative Sparse Encoder (MN3SE) which enforces both the non-negative constraint and the shift-invariant constraint on top of the traditional sparse coding criteria, and takes multi-cue to further boost the performance. The former...
We address the problem of automatically detecting anomalies in images, i.e., patterns that do not conform to those appearing in a reference training set. This is a very important feature for enabling an intelligent system to autonomously check the validity of acquired data, thus performing a preliminary, automatic, diagnosis. We approach this problem in a patch-wise manner, by learning a model to...
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...
Sparse coding is widely known as a methodology where an input signal can be sparsely represented from a suitable dictionary. It was successfully applied on a wide range of applications like the textual image Super-Resolution. Nevertheless, its complexity limits enormously its application. Looking for a reduced computational complexity, a coupled dictionary learning approach is proposed to generate...
Nonlinear dimensionality reduction (DR) is a basic problem in manifold learning. However, many DR algorithms cannot deal with the out-of-sample extension problem and thus cannot be used in large-scale DR problem. Furthermore, many DR algorithms only consider how to reduce the dimensionality but seldom involve with how to reconstruct the original high dimensional data from the low dimensional embeddings...
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