Dictionary Training Research Articles

Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation

Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler’s elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.

Denoising of magnetotelluric data using K‐SVD dictionary training

ABSTRACTMagnetotelluric is one of the mainstream exploration geophysical methods, which plays a vital role in studying deep geological structures and finding deep hidden blind ore bodies. The seriousness of human electromagnetic noise causes a large number of abnormal waveforms in the time series of measured magnetotelluric data, and the data can no longer objectively reflect the underground electrical distribution. In this work, we propose a magnetotelluric time series data processing method based on K singular value decomposition dictionary training. First, a training matrix and a to‐be‐processed matrix are built with the pending magnetotelluric signals. Then, let the K singular value decomposition dictionary training process the training matrix to obtain an over‐complete dictionary reflecting the characteristics of the pending signal. Lastly, orthogonal matching pursuit is combined with an over‐complete dictionary updated in real time to sparsely represent the to‐be‐processed matrix and remove human electromagnetic interference in the signal. Experimental results show that the method can update the over‐complete dictionary in real‐time according to the pending magnetotelluric signals, realize the self‐learning signal–noise separation of magnetotelluric signals, and effectively retain low‐frequency information. Compared with method of directions dictionary learning, remote reference method, and orthogonal matching pursuit method, the reconstructed data of the proposed method can more accurately reflect the underground electrical structure information.

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing. Compressed sensing technology is an important tool for retrieving the maximum number of HSI scenes on the ground. However, the complexity of the compressed sensing algorithm is limited by the energy and hardware of spaceborne equipment. Aiming at the high complexity of compressed sensing reconstruction algorithm and low reconstruction accuracy, an equivalent model of the invertible transformation is theoretically derived by us in the paper, which can convert the complex invertible projection training model into the coupled dictionary training model. Besides, aiming at the invertible projection training model, the most competitive task-driven invertible projection matrix learning algorithm (TIPML) is proposed. In TIPML, we don’t need to directly train the complex invertible projection model, but indirectly train the invertible projection model through the training of the coupled dictionary. In order to improve the accuracy of reconstructed data, in the paper, the singular value transformation is proposed. It has been verified that the concentration of the dictionary is increased and that the expressive ability of the dictionary has not been reduced by the transformation. Besides, two-loop iterative training is established to improve the accuracy of data reconstruction. Experiments show that, compared with the traditional compressed sensing algorithm, the compressed sensing algorithm based on TIPML has higher reconstruction accuracy, and the reconstruction time is shortened by more than a hundred times. It is foreseeable that the TIPML algorithm will have a huge application prospect in the field of HSI compression.

Sub-Pixel Mapping Model Based on Total Variation Regularization and Learned Spatial Dictionary

In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-TV: Sub-pixel Mapping based on K-SVD dictionary learning and Total Variation). The proposed approach adopts total variation as a spatial regularization parameter, to make edges smooth, and a pre-constructed spatial dictionary with the K-SVD dictionary training algorithm to have more spatial configurations at the sub-pixel level. It was tested and validated with three real hyperspectral data. The experimental results reveal that the attributes obtained by utilizing a learned spatial dictionary with isotropic total variation allowed improving the classes sub-pixel spatial localization, while taking into account pre-learned spatial patterns. It is also clear that the K-SVD dictionary learning algorithm can be applied to construct a spatial dictionary, particularly for each data set.

Learning Cross-Lingual Mappings in Imperfectly Isomorphic Embedding Spaces

One mainstream method in cross-lingual word embeddings is to learn a linear mapping between two monolingual embedding spaces using a training dictionary. Successful linear mappings require isomorphic embedding spaces. However, monolingual embedding spaces are not perfectly isomorphic, and therefore, a linear mapping cannot align them accurately. In this study, we assume that two embedding spaces are composed of near-isomorphic translation pairs (NearITP) and non-isomorphic translation pairs. Owing to the nature of similar substructures, NearITP can make linear mapping work well. Motivated by this, we design a screening strategy to identify NearITP effectively. Based on this strategy, we find that the proportion of NearITP in the commonly used training dictionary is relatively low, leading to sub-optimal results. To address this problem, we propose a general framework that can be combined with any of the mapping methods, which further boosts subsequent mapping. Experimental results demonstrate that our framework is an improvement over existing mapping-based methods, and outperforms state-of-the-art models on two public data sets. Moreover, we show that our framework can be successfully generalized to contextual word embeddings such as multilingual BERT (mBERT), and further enhances the cross-lingual properties of mBERT.

Teaching russian as a foreign language in the modern educational paradigm: training dictionaries

The work is devoted to the problem of intensification of teaching Russian as a foreign language in the process of formation and development of the lexical competence of students. The role of educational dictionaries of various types is emphasized. The Experimental Electronic Multilingual Dictionary of Military Terms is presented. It is based on ABBYY Lingvo software for teaching Russian to foreign students of military universities. His vocabulary includes a special vocabulary describing various segments of military activity (weapons, equipment, commands, military life, etc.). For each word there are translations into European languages (boi - English batttle, combat; French combat (m); Portuguese combate) and Asian languages (Laos ; Arabic ; Pashto ). The potential of an electronic multilingual dictionary in the implementation of multicultural and professionally oriented teaching of Russian as a foreign language in a non-linguistic university is identified. The role of the electronic translated multilingual thesaurus in the formation of speech professional competence, which ensures the removal of language barriers in the study of military-technical sciences by foreigners, is determined. The universality of the dictionary is characterized. It was created taking into account those national languages whose speakers receive special education in Russian military universities, and provides opportunities for the redistribution of classroom and independent work of students. Its effectiveness has been proven as a tool for modern interactive, multilingual and multicultural education.

Self-Organized Maps and High-Frequency Image Detail for MRI Image Enhancement

Magnetic resonance imaging (MRI) is a medical technology that uses powerful magnets, radio waves, and a computer to produce images of the body’s internal organs. The patient should be quiet and motionless during the scanning period, as unavoidable movements, such as breathing and heart rate, cause motion artifacts in the image, which cause contrast instability and low-resolution MRI images. Imaging in the clinical setting is performed at low resolution because the scanning time for high-resolution MR imaging would be very long and cumbersome, and imaging is also very expensive. Learning-based image superresolution methods can reconstruct MR images with optimal quality and resolution. However, these methods have problems such as the inability to find the intrinsic relationship between low- and high-resolution image patches in the training dictionary, specification of the proper amount of error in the training and testing stage due to variability in MR image contrast, inability to reconstruct objects by smoothed edges, and use of the backpropagation method in updating their weight vectors. In this paper, we propose a new superresolution method according to competitive learning-based approaches to overcome the problems of previous superresolution methods, which do not have the problems and complexities of those methods. The proposed method includes self-organizing maps as a preprocessor, the nearest neighbor algorithm as a classifier, and a high-frequency filter as a high-frequency image detail extractor. We constructed a single external dictionary from a combination of low-resolution and high-resolution feature patches and trained our SOM network. Next, we reconstruct the high-resolution image by converting the low-resolution input image into feature patch vectors, and for each vector, we find all corresponding neurons in the network and retrieve all their training feature vectors. Next, we train the nearest neighbor algorithm with the recovered vectors plus the input vector and find the best similarity vector to the input vector. After finding all the best similarity vectors to the input vectors, we reconstruct our high superresolution image. The proposed image superresolution method in practical experiments was trained, tested, and evaluated by the Div2k dataset and compared with other traditional and state-of-the-art image enhancement methods on various datasets, such as SET5, SET14, BSDS100, and URBAN100, and presented better results with higher accuracy and quality than traditional and state-of-the-art methods, both visually compared to each other by human and computational benchmarks, such as the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), to compare image superresolution algorithms. This method is best for reconstructing high-resolution magnetic resonance images that require high-frequency details and sharp edges with a smooth slope of the imaging objects in their structures. The execution time of the proposed algorithm is slower than that of the other algorithms, so we use GPU hardware and parallel programming techniques to increase the algorithm speed.

Recognition System for Libyan Entity Names

Named Entity Recognition (NER) is a computational linguistic concept that is used to find and classify appropriate nouns in a text such as person names, geographical locations, and organizations. Such a concept is fundamental in the field of natural language processing. In Libya, many private and public institutions suffer from using the proper translation of entity names from Arabic language into English. Therefore, in this paper, we are concerned with analyzing Arabic articles to extract and recognize entity names. A recognition system is developed for recognizing names of persons, academic institutions, and cities in Libya. At first, a training corpus and dictionaries are built for the intended entity names in this research. Then, the aspects of the entity names are studied, and their patterns and rules are designed. Then, the implementation is performed using Nooj linguistic language. The recognition of person names and Libyan cities and academic institutions was carried out. Statistics showed the frequencies of the appearance rate of person names, academic institutions, and cities in our training corpus. The obtained results are promised and met the research goals for tackling the problem of Arabic named entity recognition.

Hypergraph-regularized sparse representation for single color image super resolution

Sparsity-based single image super resolution method generates the High-Resolution (HR) output via a corresponding dictionary from the Low-Resolution (LR) input. However, most of these existing methods ignore the complementary information from color channels, which causes the loss of a valid prior and the limitation of HR image quality improvement. In this paper, hypergraph regularization is first incorporated with Joint Color Dictionary Training (JCDT) model and HR image reconstruction (HRIR) model. A novel Hypergraph-regularized Sparse coding-based Super Resolution (HG-ScSR) is proposed. This regularization can not only focus on the illuminance information, but also exploit the self-channel and cross-channel information of three color RGB channels from high-resolution image patches. Especially, the complex relationship is explored among every color image patch pixel and the consistency of the similar pixels is enforced. Both simulated and real data experiments verify the higher performance of the proposed HG-ScSR.

Fast multi‐spectral image super‐resolution via sparse representation

Sparse reconstruction is used to solve the inverse problem of single image super-resolution (SR) as a patch-based sparsity promoting regularisation problem. A coupled trained overcomplete dictionary from high-resolution (HR) and low-resolution (LR) image patches containing significant features is proposed using sparse representation to produce HR patches from their LR counterparts. In this study, the authors develop a multi-core single image SR technique for LR multi-spectral images based on patch-wise sparse representation coupled with morphological component analysis driven feature extraction. Simulations are carried out to evaluate the proposed method using real remote sensing images of a few Indian satellites, RESOURCESAT-2 and CARTOSAT-2, as well as other satellites, such as QuickBird etc. Results are also compared with other existing SR methods to establish the superiority of the proposed method in terms of both objective metrics and visual analysis.

A nonlinear solution to 3D seismic data conditioning using trained dictionaries

Seismic analysis for reservoir characterization has been a primary focus for the geophysical community for decades. One of the critical steps in delivering high-quality processed seismic data for seismic analysis is to remove undesirable prestack seismic phenomena prior to amplitude variation with offset (AVO) analysis. Contrary to the conventional approach, which is mainly 2D gather-based and assumes flat events, we have developed a 3D nonlinear approach with a single principle: the 3D geologic structure should be invariant from offset to offset. Trained dictionaries, generated by 3D complex wavelet transformation over pilot volumes, are progressively constructed by stacking over selected offsets or angles. A sparse nonlinear approximation using the L0 norm is imposed on the data against the trained dictionaries after applying a 3D complex wavelet transform to the data. The final step is to apply an inverse 3D complex wavelet transform to the sparsified coefficients to return to the data space. This workflow is repeated for all offsets or angles. The workflow is automatic and requires minimal user input, resulting in a fast and efficient process. Multiple field data examples have demonstrated significant signal-to-noise ratio uplift, AVO and azimuthal AVO conservation, preservation of steeply dipping structural events, and multiple suppression. The processing time is significantly shorter compared with alternative conventional processes.

A novel hyper-chaotic image encryption with sparse-representation based compression

This digital era uses lots of images in communication, which are confidential and large in volume. The transmission channel thus arises the question of security as well as transmission load. To date, several works are there to solve this issue. However, they fail to provide a single unit that gives these two facilities in a unified way. This paper presents a novel technique of unification of image compression and encryption in a single module via sparse-representation of image frames and hyper-chaotic encryption techniques. In this work, we have proposed a method to estimates the sparse vectors of a given image using a regularized trained over-complete dictionary and encrypt the non-zero coefficients of the sparse vectors using key-streams generated by a hyper-chaotic system. This sparse coding based encryption technique provides a higher compression ratio (CR) compared to some recently proposed techniques on one side and increases security level on the other side. Moreover, the security is strengthened by using this key-sequence in different steps in the encryption scheme. Thus the compressed-encrypted outputs are stronger than simple chaotic encryption against any intruder. The efficiency and authenticity of the proposed algorithm are verified through several quality-index e.g. entropy, CR, etc. The resistivity of the proposed algorithm toward the known or chosen-plaintext attack is also analyzed. The results of the key sensitivity test, and cropping attack test also ensure the authors’ claim. The comparison of the proposed technique with some recently published works justifies the reliability of this work.

Semi-supervised transient noise suppression using OMLSA and SNMF algorithms

Transient interferences such as keystrokes, mouse clicks and hammering pose a significant challenge in the single channel speech enhancement due to their abrupt and non-continuous nature. Traditional noise suppression algorithms and even many non-stationary noise reduction algorithms do not adequately suppress transient interference. Therefore, in this work, we propose a semi-supervised single channel transient noise suppression method to effectively suppress the transient interference without significant audible distortion. The proposed algorithm consists of training and testing stages. In the training stage, the proposed technique first uses the optimally modified-log spectral amplitude (OMLSA) estimator to estimate the transient noise from the noisy speech signal. After that, we eliminate the residual speech components from the estimated noise obtained from OMLSA based on the correlation coefficient, by taking correlation between the estimated noise with the available clean speech data from the dataset passed through the voice activity detector for silence zones removal. Afterwards, we use this noise for training the noise dictionary in sparse non-negative matrix factorization. Clean speech data is used for speech dictionary training. In the enhancement stage, the dictionaries are fixed and concatenated, to obtain the corresponding activation matrices. The clean speech dictionary and the corresponding weight matrix are used to reconstruct the estimated speech. The experimental results reveal that the proposed algorithm provided better performance compared to other existing algorithms in the speech quality evaluation metrics.

Image Defogging Algorithm Based on Sparse Representation

Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.

Frequency Event Categorization in Power Distribution Systems Using Micro PMU Measurements

Frequency is an important metric for power stability. To enhance the situational awareness in power distribution systems, this paper proposes a data-driven approach that utilizes the data from micro phasor measurement units (μPMU) to categorize frequency events observed in distribution feeders. The proposed approach has three layers, each of which analyzes if a frequency event is propagated from the transmission system, formed inside a distribution feeder or falsely produced by instantaneous frequency (IF) estimation algorithms, respectively. In the first layer, the Granger causality is utilized to compare the causal relations from measurements across different locations to decide if an event is propagated from the transmission side. In the second layer, a trained sparse coding dictionary is utilized to unmix the physical causes of a frequency event. In the third layer, a moving z-score based event-pair detector is applied to identify if a frequency event is algorithm-induced. The proposed approach is tested using the μPMU measurements collected from Lawrence Berkeley National Laboratory (LBNL) and Riverside Public Utility (RPU).

Description of Synonyms in Linguodidactics: Integrative Approach (based on the Synonymous Row of Verbs with a Common Meaning ‘do nothing’)

The article is devoted to the description of a synonymous series of Russian verbs with a dominant idle against the background of the Vietnamese language for further presentation of Russian units in a Vietnamese audience. The approaches to the study of synonymous units traditionally highlighted in Russian linguistics are considered. The relevance of an integrative approach to the analysis of synonyms for linguodidactic purposes is substantiated. The novelty of the study is that such a comprehensive approach allows us to analyze synonyms at the level of the language system, discourse and language consciousness of native speakers. The relevance of the study is due to the fact that the synonymous row under study is included in the lexico-semantic group of behavior verbs, nuclear for the Russian language, as well as the lack of training dictionaries of Russian synonyms for foreign students. The authors determine the structure of the studied synonymic row in the Russian language, analyze the semantics, typical situations of use, the stylistic classification of units according to the components that make up the synonymic series. Particular attention is paid to data from a survey of native Russian speakers. A comparative analysis of Russian synonyms and their translation equivalents in the Vietnamese language is presented. Relevant parameters of the characteristics of the members of the synonymous row are identified to optimize the work with these units in the Vietnamese audience. Linguistic and methodological recommendations on the presentation of Russian synonyms in a Vietnamese audience are offered.

Reconstructing the lexicographical triangle through teaching dictionary literacy to teachers of English

This paper attempts to establish the need to (re)introduce use of dictionary training into formal EFL teaching. Reference is made to dictionaries for the foreign learners of English (EFL dictionaries), being first conceptualised and compiled as early as the 1930s. The supposition was that the use of dictionary held an integral part in EFL teaching and learning, particularly in vocabulary learning, and that the English teacher would teach students to make full necessary use of the dictionary. Both the EFL dictionary and EFL teaching methodology target the same users. Indeed, prominent EFL lexicographers, during the time like Michael West and A.S. Hornby, were EFL teachers and teacher trainers, who had first-hand experience of students’ learning difficulties and teachers’ teaching needs. Dictionaries like the New Method English Dictionary (1935) targeted mainly Indian learners of English and the Idiomatic and Syntactic English Dictionary (1942) the Japanese mostly. As EFL lexicography and teaching methodologies have been evolving on their own paths in the past decades, the relationship between the two areas has become less dynamic and supportive (complementary). The broken relationship has gravely impacted the use of the dictionary among EFL learners who follow a structured language curriculum. One suggestion to bridge the gap between the dictionary and EFL teachers is to provide training to English language teachers on “dictionary literacy”. When equipped with such knowledge, EFL teachers would be empowered to select and evaluate available English dictionaries to facilitate their own teaching and educate their students to use the dictionary in their learning.

Multiview Automatic Target Recognition for Infrared Imagery Using Collaborative Sparse Priors

The low resolution of infrared (IR) images makes feature extraction for classification of a challenging work. Learning-based methods, therefore, are preferred to be used on such raw imagery. In this article, in order to avoid difficulties in feature extraction, a novel multitask extension of the widely used sparse-representation-classification (SRC) method is proposed in both single and multiview set-ups. That is, the test sample could be a single IR image or images from different views. In both single-view and multiview scenarios, we try to employ collaborative spike and slab priors. This is because the traditional sparsity-inducing measures such as the $l_{0}$ -row pseudonorm makes it hard to capture the sparse structure of the coefficient matrix when expanded in terms of a training dictionary, and the priors are proved to be able to capture fairly general sparse structures. Furthermore, a joint prior and sparse coefficient estimation method (JPCEM) is proposed for the first time in this article in order to alleviate the need to handpick prior parameters required before classification. Multiple experiments are conducted on a synthetic Comanche Forward Looking IR (FLIR) Automatic Target Recognition (ATR) database collected by Army Research Lab and a challenging mid-wave IR (MWIR) image ATR database made available by the U.S. Army Night Vision and Electronic Sensors Directorate. The final results substantiate the merits of the proposed JPCEM through comparisons with other state-of-the-art methods, including both the ones based on SRC and the ones constructed using deep learning frameworks.

A Learning-Enhanced Two-Pair Spatiotemporal Reflectance Fusion Model for GF-2 and GF-1 WFV Satellite Data.

Since requirements of related applications for time series remotely-sensed images with high spatial resolution have been hard to be satisfied under current observation conditions of satellite sensors, it is key to reconstruct high-resolution images at specified dates. As an effective data reconstruction technique, spatiotemporal fusion can be used to generate time series land surface parameters with a clear geophysical significance. In this study, an improved fusion model based on the Sparse Representation-Based Spatiotemporal Reflectance Fusion Model (SPSTFM) is developed and assessed with reflectance data from Gaofen-2 Multi-Spectral (GF-2 MS) and Gaofen-1 Wide-Field-View (GF-1 WFV). By introducing a spatially enhanced training method to dictionary training and sparse coding processes, the developed fusion framework is expected to promote the description of high-resolution and low-resolution overcomplete dictionaries. Assessment indices including Average Absolute Deviation (AAD), Root-Mean-Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), spectral angle mapper (SAM), structure similarity (SSIM) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS) are then used to test employed fusion methods for a parallel comparison. The experimental results show that more accurate prediction of GF-2 MS reflectance than that from the SPSTFM can be obtained and furthermore comparable with popular two-pair based reflectance fusion models like the Spatial and Temporal Adaptive Fusion Model (STARFM) and the Enhanced-STARFM (ESTARFM).

The Application and Effectiveness of Prototype Theory in The New Oxford English-Chinese Dictionary

Abstract The New Oxford English-Chinese Dictionary (NOECD) is a bilingualized dictionary based on the New Oxford Dictionary of English (NODE), which is likely the first dictionary to claim explicitly to put prototype theory into dictionary making. Assessments of the effectiveness of this move vary, but so far, no empirical study has been conducted to examine it. This paper reports two studies of the application of prototype theory in NOECD. The first regards the use of the theory in defining and sense organising, and the second concerns users’ perception of the effectiveness of the organisation of sense. The first study is qualitative, and it examines how prototype theory is exhibited in defining and sense organising. The second study is empirical and consists of a test of dictionary users’ vocabulary retention and some follow-up questions. In this second study, it was found that the prototype strategy adopted by NOECD, of identifying a core sense and grouping subsenses around it, contributed little to improving user efficiency in memorising word meanings. Several possible reasons are proposed to account for the ineffectiveness: the influence of L1, limitations of prototype theory, users’ inadequate dictionary skills and others. It is concluded that these challenges could be addressed by compiling nation-specific dictionaries for specific users. Dictionary training should be an inseparable part of language learning to improve users’ dictionary skills and awareness.