Heuristic dual-tree wavelet thresholding for infrared thermal image denoising of underground visual surveillance system

Lin Zhang; Xiaomin Xie; Shang Feng; Minzhou Luo

doi:10.1117/1.OE.57.8.083102

17 August 2018 Heuristic dual-tree wavelet thresholding for infrared thermal image denoising of underground visual surveillance system

Lin Zhang, Xiaomin Xie, Shang Feng, Minzhou Luo

Author Affiliations +

Funded by: Fundamental Research Funds for Central Universities, Changzhou Key Laboratory of Aerial Work Equipment and Intelligent Technology

Optical Engineering, Vol. 57, Issue 8, 083102 (August 2018). https://doi.org/10.1117/1.OE.57.8.083102

Abstract

To remove noise from infrared thermal images captured in underground mining working face under low luminance and dusty environment, a nonreference infrared thermal image denoising method based on heuristic dual-tree wavelet thresholding is proposed. The threshold is optimized through estimating noise variance in wavelet domain using an improved chaotic drosophila algorithm (CDOA), which is promoted by a spatial–spectral entropy based metric. The basic DOA, genetic algorithm, particle swarm optimization algorithm, and virus colony search algorithm are implemented to compare the convergence rate and optimization ability of improved CDOA. Moreover, other representative denoising methods, such as BM3D, BLS-GSM, fast translation invariant, and nonlocal Bayes, are also applied for comparison. Comparison result proves effectiveness and superiority of the proposed method. Finally, the proposed method is applied in infrared thermograph-based visual surveillance system, and the denoising results also prove the state-of-art performance.

Citation Download Citation

Lin Zhang, Xiaomin Xie, Shang Feng, and Minzhou Luo "Heuristic dual-tree wavelet thresholding for infrared thermal image denoising of underground visual surveillance system," Optical Engineering 57(8), 083102 (17 August 2018). https://doi.org/10.1117/1.OE.57.8.083102

Received: 6 April 2018; Accepted: 13 July 2018; Published: 17 August 2018

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