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1

Local Characterisation and Detection of Woven Fabric Texture Based on a Sparse Dictionary

Wu Ying, Wang Ren, Lou Lin, Wang Lali, Wang Jun

Fibres & Textiles in Eastern Europe

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2022

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Vol. 30, no. 3

33--40

EN

To achieve enhanced accuracy of fabric representation and defect detection, an innovative approach using a sparse dictionary with small patches was used for fabric texture characterisation. The effectiveness of the algorithm proposed was tested through comprehensive characterisation by studying eight weave patterns: plain, twill, weft satin, warp satin, basket, honeycomb, compound twill, and diamond twill and detecting fabric defects. Firstly, the main parameters such as dictionary size, patch size, and cardinality T were optimised, and then 40 defect-free fabric samples were characterised by the algorithm proposed. Subsequently, the Impact of the weave pattern was investigated based on the representation result and texture structure. Finally, defective fabrics were detected. The algorithm proposed is an alternative simple and scalable method to characterise fabric texture and detect textile defects in a single step without extracting features or prior information.

2

Multichannel seismic impedance inversion driven by logging–seismic data

Li Xinyu, Wang Yaojun, Liu Yu, Cai Hanpeng

Acta Geophysica

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2021

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Vol. 69, no. 6

2261--2274

EN

The prior information constrained impedance inversion is an important tool to improve the inversion effect. With the traditional constrained prior information extracted from logging data by the analytic formula, it is difficult to accurately describe the information of a complex reservoir. In addition, the traditional inversion method is trace-by-trace, which ignores the lateral information contained in seismic data. This paper presents a multichannel seismic impedance inversion method combining logging and seismic. In this method, the dictionary learning method is used to extract the vertical prior information of the formation from the logging data. At the same time, we can learn the dip information from seismic data cube. Under the framework of multichannel inversion, regularization and sparse representation technology are used to simultaneously add the vertical and the transverse distribution prior information into the inversion process. Block coordinate descent method is used to solve the multichannel inversion problem, making the seismic inversion efficient. This method excavates the spatial prior information in a data-driven way and is used for constrained inversion, avoiding the false prior cognition caused by manual interpretation. Through the model and field data testing, it is verified that this method is effective.

3

Single Image Super-Resolution through Sparse Representation via Coupled Dictionary learning

Patel Rutul, Thakar Vishvjit, Joshi Rutvij

International Journal of Electronics and Telecommunications

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2020

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Vol. 66, No. 2

347--353

EN

Single Image Super-Resolution (SISR) through sparse representation has received much attention in the past decade due to significant development in sparse coding algorithms. However, recovering high-frequency textures is a major bottleneck of existing SISR algorithms. Considering this, dictionary learning approaches are to be utilized to extract high-frequency textures which improve SISR performance significantly. In this paper, we have proposed the SISR algorithm through sparse representation which involves learning of Low Resolution (LR) and High Resolution (HR) dictionaries simultaneously from the training set. The idea of training coupled dictionaries preserves correlation between HR and LR patches to enhance the Super-resolved image. To demonstrate the effectiveness of the proposed algorithm, a visual comparison is made with popular SISR algorithms and also quantified through quality metrics. The proposed algorithm outperforms compared to existing SISR algorithms qualitatively and quantitatively as shown in experimental results. Furthermore, the performance of our algorithm is remarkable for a smaller training set which involves lesser computational complexity. Therefore, the proposed approach is proven to be superior based upon visual comparisons and quality metrics and have noticeable results at reduced computational complexity.

4

A robust data driven AVO inversion with logarithm absolute error loss function

Du Siyuan, Zhang Jiashu, Hu Guangmin

Acta Geophysica

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2020

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Vol. 68, no. 2

445--458

EN

Amplitude variation with ofset (AVO) inversion is a widely used approach to obtain reliable estimates of elastic parameter. Tikhonov and total variation regularization are commonly used methods to address ill-posed problem of AVO inversion. However, these model-driven methods are only for special geological structure such as smoothness or blockiness. In this letter, a robust data-driven-based regularization method with logarithm absolute error loss function (DDI-Log) for AVO inversion is proposed. In DDI-Log, the information of well-log data and the complex geology are considered in a sparse representation framework. In pre-stack seismic data, outlier noise can negatively infuence inversion results. Thus, diferent from the previous data-driven inversion based on L2 norm loss function, we extend the logarithm absolute error function as the loss function. In the iteration, a new spectral PRP conjugate gradient method is used to solve the large-scale optimization problem. The synthetic data and feld data tests illustrate that the proposed approach is robust against outlier noise and that the resolution and accuracy of the solutions are improved.

5

Large-scale hyperspectral image compression via sparse representations based on online learning

Ülkü İ., Kizgut E.

International Journal of Applied Mathematics and Computer Science

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2018

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Vol. 28, no. 1

197--207

EN

In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.

6

Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection

Andrysiak T., Saganowski Ł.

Schedae Informaticae

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2015

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Vol. 24

63--71

EN

In this article we present the use of sparse representation of a signal and incoherent dictionary learning method for the purpose of network traffic analysis. In learning process we use 1D INK-SVD algorithm to detect proper dictionary structure. Anomaly detection is realized by parameter estimation of the analyzed signal and its comparative analysis to network traffic profiles. Efficiency of our method is examined with the use of extended set of test traces from real network traffic. Received experimental results confirm effectiveness of the presented method.