Image processing technology based on OMP reconstruction optimization algorithm

Article type: Research Article

Authors: Tan, Jie

Affiliations: Faculty of Engineering, Tongren Polytechnic College, Tongren, China | E-mail: [email protected]

Correspondence: [*] Corresponding author: Faculty of Engineering, Tongren Polytechnic College, Tongren, China. E-mail: [email protected].

Abstract: With the widespread application of digital images, image processing technology plays an important role in fields such as computer vision and image analysis. Based on the orthogonal matching pursuit algorithm, an image processing method is proposed. In the process, sparse representation and reconstruction algorithm are used for image compressed sensing to complete image sampling operation. Afterwards, the theory of overcomplete sparse representation is introduced to optimize sparse representation, and an overcomplete dictionary is used to remove Gaussian noise, achieving the goal of image processing. The experimental results indicate that the research method do not show significant deficiencies in signal reconstruction when testing reconstructed signals under sparsity of 8; When testing the calculation time, the calculation time of the research method is about 0.212 s when the sparsity is 5 in the Lenna; In the error test, the mean square difference of the research method in the Lenna is stable at about 14.6; When conducting application analysis, the variance eigenvalues of the research method remained below 9.4. This indicates that the research method has good performance and can effectively process images, providing new technical support for image processing.

Keywords: Image processing, orthogonal matching pursuit, sparse representation, compressed sensing, gaussian noise

DOI: 10.3233/JCM-247284

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 3, pp. 1741-1753, 2024