Title: Adaptive surveillance image enhancement algorithm based on wavelet transform

Authors: Lan Li

Addresses: Department of Urban Rail Transit and Information Engineering, Anhui Communications Vocational and Technical College, Hefei 230051, Anhui, China; School of Computer and Information, Hefei University of Technology, Hefei 230051, Anhui, China

Abstract: In order to improve the definition and signal-to-noise ratio of surveillance image, an adaptive surveillance image enhancement algorithm based on wavelet transform is proposed. First, FWT filter is used to decompose the monitoring image signal, and wavelet reconstruction is used to reconstruct the adaptive monitoring image. Secondly, Sobel operator is introduced to improve the NL means algorithm, and the improved NL means algorithm is used to remove the noise in the adaptive surveillance image. Finally, in the scale space, according to the grey calculation results, the adaptive surveillance image disparity map is decomposed and enhanced according to the decomposed disparity map. The experimental results show that the proposed enhancement algorithm can improve the definition and signal-to-noise ratio of the surveillance image, and the maximum signal-to-noise ratio is 61.5 dB.

Keywords: wavelet transform; adaptive surveillance image; image enhancement; image denoising.

DOI: 10.1504/IJCISTUDIES.2023.132487

International Journal of Computational Intelligence Studies, 2023 Vol.12 No.1/2, pp.92 - 104

Received: 18 Oct 2022
Accepted: 03 Dec 2022
Published online: 24 Jul 2023 *

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