Dictionary-based intra-prediction framework for image compression via sparse representation

Summary form only given. Images and video are being extensively used in numerous areas such as video-conferencing, multimedia documentation, telemedicine, high definition television (HDTV) etc. These diverse applications can benefit from the design of a family of image compression algorithms that address their specific needs. We propose a framework for wavelet-based image compression that leads to a family of image compression schemes, and facilitates a comparative study of the complexity, compression ratio, and other properties of this family. The embedded zerotree wavelet (EZW) and the web of wavelets (WW) methods fall into this family.

Sparse representation-based single image super-resolution (SISR) methods use a coupled overcomplete dictionary trained from high-resolution images/image patches. Since remote sensing (RS) satellites capture images of large areas, these images usually have poor spatial resolution and obtaining an effective dictionary as such would be very challenging. Moreover, traditional patch-based sparse representation models for reconstruction tend to give unstable sparse solution and produce visual artefact in the recovered images. To mitigate these problems, in this article, we have proposed an adaptive joint sparse representation-based SISR method that is dependent only on the input low-resolution image for dictionary training and sparse reconstruction. The new model combines patch-based local sparsity and group sparse representation-based nonlocal sparsity in a single framework, which helps in stabilizing the sparse solution and improve the SISR results. The experimental results are evaluated both visually and quantitatively for several RGB and multispectral RS datasets, where the proposed method shows improvements in peak signal-to-noise ratio by 1–4 dB and 2–3 dB over the state-of-the-art sparse representation- and deep learning-based SR methods, respectively. Land cover classification applied on the super-resolved images further validate the advantages of the proposed method. Finally, for practical RS applications, we have performed parallel implementation in general purpose graphics processing units and achieved significant speed ups (30–40×) in the execution time.

This paper addresses the problem of interferometric noise reduction in Synthetic Aperture Radar (SAR) interferometry based on sparse and redundant representations over a trained dictionary. The idea is to use a Proximity-based K-SVD (ProK-SVD) algorithm on interferometric data for obtaining a suitable dictionary, in order to extract the phase image content effectively. We implemented this strategy on both simulated as well as real interferometric data for the validation of our approach. For synthetic data, three different training dictionaries have been compared, namely, a dictionary extracted from the data, a dictionary obtained by a uniform random distribution in , and a dictionary built from discrete cosine transform. Further, a similar strategy plan has been applied to real interferograms. We used interferometric data of various SAR sensors, including low resolution C-band ERS/ENVISAT, medium L-band ALOS, and high resolution X-band COSMO-SkyMed, all over an area of Mt. Etna, Italy. Both on simulated and real interferometric phase images, the proposed approach shows significant noise reduction within the fringe pattern, without any considerable loss of useful information.

The human visual system is sensitive to structural information in images, and modeling this information has been regarded as useful for predicting their perceptual quality. In this study, we propose a no-reference (NR) image quality assessment (IQA) method based on a sparse representation of the distribution of structural information. The grayscale fluctuation map of an image is first calculated and divided into patches of fixed size that are rearranged into column vectors, which are regarded as structural elements of the image. Following this, using sparse coding, these structural elements can be represented by sparse representation coefficients and a trained dictionary. By using the former, a probability vector for observing different elements in the trained dictionary can then be obtained. Finally, the prediction model is trained using support vector regression. The results of experiments to test the proposed method show that it can accurately predict humans’ perception of image quality and is competitive in comparison with prevalent NR–IQA methods.

Purpose: To present a methodology for reducing cone beam CT (CBCT) dose using low dose imaging protocols and sparse representation techniques to achieve similar image quality as regular or high dose imaging protocols. Method and Materials: The frequent usage of CBCT raises the concern of excessive non‐therapeutic dose delivered to patients, especially to pediatric patients. Sparse representation (SR) was applied to low dose CBCT imaging, achieved by using low mAs, to suppress image noise and streak artifacts. The denoising algorithm was applied to low dose clinical images based on the concept of sparse representation using the training dictionary, which was constructed from the regular dose clinical images to approximate the anatomical site of interest. Validation was carried out using two phantoms (a CATPHAN phantom and an anthropomorphic head & neck phantom) and clinical prostate images. Comparisons with the modified edge preserving curvature anisotropic diffusion filter (MCDF) were carried out. Results: For CATPHAN phantom images, the SR algorithm outperformed the MCDF algorithm in terms of contrast to noise ratio (CNR). CNR was improved by an average of 32% for the SR algorithm. In contrast, MCDF only improved CNR by an average of 2.4%. Visual assessments of the anthropomorphic head & neck phantom and patient images indicated the SR algorithm not only suppressed the image noise but also preserved fine details of anatomical structures and was significantly better than MCDF. Conclusion: SR can be used to reduce image noise and CBCT imaging artifacts present in low dose CBCT images. Dose reductions up to 88% were observed without loss of image quality based on visual assessment.

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. Classification is achieved by representing the extracted features of the test data as a linear combination of entries in the dictionary. We propose joint weighted dictionary learning and classifier training (JWDL-CT) approach which simultaneously learns from a set of training samples along with weight vectors that correspond to the atoms in the learnt dictionary. The components of the weight vector associated with an atom represent the relationship between the atom and each of the classes. The weight vectors and atoms are jointly obtained during the dictionary learning. In the proposed method, a constraint is imposed on the correlation between the atoms to decrease the similarity between these atoms. The proposed dictionary learning objective function enhances the class-discrimination capabilities of individual atoms that renders the designed dictionaries especially suitable for classification of query images with very sparse representation. Experiments conducted on the West Virginia University (WVU) and the University of Notre Dame (UND) datasets for ear recognition show that the proposed method outperforms other state-of-the-art classifiers.

By incorporating the priors of image positions, position-patch based face hallucination methods can produce high-quality results and save computation time. These methods represent the test image patch as a linear combination of the same position patches in a training dictionary, and the key issue is how to obtain the optimal coefficients. Due to stability and accuracy issues, methods based on least square estimation or sparse representation (SR) proposed so far are not satisfactory. In this paper, we improve existing SR methods by exploiting similarity between the test and training patches. In particular, we impose a similarity constraint (in terms of the distance between the test patch and bases in the dictionary) on the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> minimization regularization term and obtain the coefficients by solving a weighted SR problem. We also provide a new prospective on weighted SR and investigate its robustness to illumination variations. Experiments on commonly used database demonstrate that our method outperforms state of the art.

The sparsity is an attractive property that has been widely and intensively utilized in various image processing fields (e.g., robust image representation, image compression, image analysis, etc.). Its actual success owes to the exhaustive mining of the intrinsic (or homogenous) information from the whole data carrying redundant information. From the perspective of image representation, the sparsity can successfully find an underlying homogenous subspace from a collection of training data to represent a given test sample. The famous sparse representation (SR) and its variants embed the sparsity by representing the test sample using a linear combination of training samples with L0-norm regularization and L1-norm regularization. However, although these state-of-the-art methods achieve powerful and robust performances, the sparsity is not fully exploited on the image representation in the following three aspects: 1) the within-sample sparsity, 2) the between-sample sparsity, and 3) the image structural sparsity. In this paper, to make the above-mentioned multi-context sparsity properties agree and simultaneously learned in one model, we propose the concept of consensus sparsity (Con-sparsity) and correspondingly build a multi-context sparse image representation (MCSIR) framework to realize this. We theoretically prove that the consensus sparsity can be achieved by the L∞-induced matrix variate based on the Bayesian inference. Extensive experiments and comparisons with the state-of-the-art methods (including deep learning) are performed to demonstrate the promising performance and property of the proposed consensus sparsity.

Pansharpening is a process of combining a high-resolution Panchromatic (PAN) image with a low-resolution Multispectral (MS) image to obtain a high-resolution MS image. Pansharpening can not only overcome the physical and technical limitations of satellite sensors, but also improve the quality of images and obtain a more detailed description of the scene. In this paper, a novel method of Pansharpening is proposed which adopts the ARSIS concept. The spatial information missing in the low-resolution MS image can be extracted from the PAN image. This is achieved by image decomposition based on sparse representation. Then, the information is injected into the MS bands by the details injection model. In the dictionary training, we adapt Online Sparse Dictionary Learning (OSDL), which can shorten the dictionary training time and improve the fusion image effect. Experimental results show that the fusion image of the proposed method has stronger spectral performance and more detailed spatial information.

Blade tip timing (BTT) is a promising technique for the blade vibration measurement. Since under-sampling is an inherent drawback of BTT measurement, compressed sensing theory which enables the recovery of sparse signals provides a novel method to the reconstruction of BTT signals. Considering the variability of the excitation method and the material of blades under actual working condition, the instantaneous displacement of each blade could not be merely modeled as multiple component sine waves. Therefore, proper dictionary is needed for the representation of sparse blade tip vibration signals. We proposed a method for reconstructing irregular blade tip deflection signals in this paper which consists of four key steps. First, based on the optical probe placements, measurement matrix for compressed sensing is determined. Secondly, a mathematical model of BTT signals is proposed based on sparse representation. Third, combining the K-SVD dictionary learning method, training dictionary is realized through experimental results of measured blade displacements under nonlinear case. Finally, blade tip-timing signals are recovered by Basis Pursuit Algorithm. Numerical simulation examples of strain gage signals from vibration experiments are performed to prove the validation of mentioned methods. The main advantage of this method is the well adapting to feature identification of blades vibrations in strong nonlinear case.

A parameterized fuzzy adaptive K-SVD approach for the multi-classes study of pursuit algorithms

Sparse representations using overcomplete dictionaries has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. The K-SVD algorithm is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. However, the existing K-SVD algorithm is employed to dwell on the concept of a binary class assignment meaning that the multi-classes samples are assigned to the given classes definitely. The work proposed in this paper provides a novel fuzzy adaptive way to adapting dictionaries in order to achieve the fuzzy sparse signal representations, the update of the dictionary columns is combined with an update of the sparse representations by incorporated a new mechanism of fuzzy set, which is called fuzzy K-SVD. Experimental results conducted on the ORL and Yale face databases demonstrate the effectiveness of the proposed method.

Performing the image compression over the resource constrained hardware is quite a challenging task. Although, there has been various approaches being carried out towards image compression considering the hardware aspect of it, but still there are problems associated with the memory acceleration associated with the entire operation that downgrade the performance of the hardware device. Therefore, the proposed approach presents a cost effective image compression mechanism which offers lossless compression using a unique combination of the non-linear filtering, segmentation, contour detection, followed by the optimization. The compression mechanism adapts analytical approach for significant image compression. The execution of the compression mechanism yields faster response time, reduced mean square error, improved signal quality and significant compression ratio performance.

In this paper, a novel overcomplete dictionary training method which is based on empirical mode decomposition is presented. The IMFs by empirical mode decomposition take part in the training of overcomplete dictionary, and K-SVD algorithm is adopted in the training process. Simulation results show that, compared with the dictionary trained directly from the original speech signals, the overcomplete dictionary has sparser representation for the speech signals, and thus has higher reconstructed speech quality.

In order to solve the problem of time-consuming dictionary training process, we propose an image super-resolution reconstruction approach based on fusion of K-SVD algorithm and semi-coupled dictionary learning framework. In this paper, we use K-SVD algorithm to training the dictionary pair in the semi-coupled dictionary learning model. In comparison with the existing methods, experiment results on image reconstruction show that the proposed algorithm reduce dictionary training time nearly four times, while improve the quality of reconstructed images.