An Orthogonal Matching Pursuit Algorithm Based on Singular Value Decomposition

Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing methods such as wavelet and fast Fourier transform being imposed by orthogonal basis. Sparse decomposition provides an effective approach for feature extraction of intricate vibration signals collected from rotating machinery. Self-learning over-complete dictionary and pre-defined over-complete dictionary are the two dictionary construction modes of sparse decomposition. Normally, the former mode owns the virtues of much more adaptive and flexible than the latter one, and several kinds of classical self-learning over-complete dictionary methods have been arising in recent years. K-means singular value decomposition is a classical self-learning over-complete dictionary method and has been used in image processing, speech processing, and vibration signal processing. However, K-means singular value decomposition has relative low reconstruction accuracy and poor stability to enhance the desired features. To overcome the above-mentioned shortcomings of K-means singular value decomposition, a new K-means singular value decomposition sparse representation method based on traditional K-means singular value decomposition method was proposed in this article, which uses the sparse adaptive matching pursuit algorithm and an iterative method based on the minimum similarity of atomic structure. The effectiveness and advantage of the proposed method were verified through simulation and experiment.

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Hazardous Gas Emission Monitoring Based on High-Resolution Images

Journal of Spectroscopy ◽

10.1155/2018/2698025 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7

Author(s):

Wenjian Chen ◽

Yi Wang ◽

Xuan Li ◽

Wei Gao ◽

Shiwei Ma ◽

...

Keyword(s):

High Resolution ◽

Matching Pursuit ◽

Singular Value ◽

Reconstruction Method ◽

Crucial Importance ◽

Gas Emission ◽

Depth Data ◽

High Resolution Images ◽

Matching Pursuit Algorithm ◽

Value Decomposition

Air pollution presents unprecedentedly severe challenges to humans today. Various measures have been taken to monitor pollution from gas emissions and the changing atmosphere, of which imaging is of crucial importance. By images of target scenes, intuitional judgments and in-depth data are achievable. However, due to the limitations of imaging devices, effective and efficient monitoring work is often hindered by low-resolution target images. To deal with this problem, a superresolution reconstruction method was proposed in this study for high-resolution monitoring images. It was based on the idea of sparse representation. Particularly, multiple dictionary pairs were trained according to the gradient features of samples, and one optimal pair of dictionaries was chosen to reconstruct by judging the weighting of the information in different directions. Furthermore, the K-means singular value decomposition algorithm was used to train the dictionaries and the orthogonal matching pursuit algorithm was employed to calculate the sparse coding coefficients. Finally, the experiment’s results demonstrated its advantages in both visual fidelity and numerical measures.

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