Abstract
A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. Firstly, the dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. Then, the discrete wavelet transform (DWT) is utilized to obtain the low frequency sub-band (LFS) and high frequency sub-band (HFS). Next, the wavelet shrinkage threshold is applied to calculate the wavelet threshold corresponding to the HFS at different scales. Meanwhile, a new Garrate threshold function that introduces adjustment factor and enhancement coefficient is designed to adaptively de-noise and enhance the HFS coefficients, and the Gamma function is employed to correct the LFS coefficients. Finally, the PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can not only significantly improve the overall brightness and contrast of the degraded image but also suppresses the noise of dust and spray and enhances the image details. Compared with the similar algorithms of STFE, GTFE, CLAHE, SSR, MSR, DGR, and MSWT algorithms, the indicator values of comprehensive performance of the proposed algorithm are increased by 205%, 195%, 200%, 185%, 185%, 85%, 140%, and 215%, respectively. Moreover, compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments.
Highlights
With the increasing contradiction between mine safety production and economic benefits, the related safety issues have aroused wide concern of the country and society [1, 2].e processing and analysis of the intelligent monitoring video in mine is a necessary prerequisite for studying the location and status of moving objects such as underground personnel, vehicles, and equipment [3,4,5]
The images of surveillance videos are susceptible to dust and spray, low illumination or uneven lighting of artificial light source, and other environmental factors, resulting in poor image quality captured by the camera [6, 7]. is directly affects the mine dispatch center’s accurate grasp of the actual underground situation and subsequent data analysis. erefore, in order to better present the scene information of coal mine, improve the visual effect of the image, and promote the application of the underground video monitoring system in the coal mine safety production and intelligent analysis, it is of great significance to study the enhancement of the degraded image in coal mine
For the drawbacks of the above existing algorithms, this paper proposes a method for degraded image enhancement that combines dual-domain-adaptive wavelet and improved fuzzy transform, in which a Garrate threshold function that introduces adaptive adjustment factor and enhancement coefficient is designed, and an improved PAL fuzzy transform function is proposed
Summary
Degraded Image Enhancement Using Dual-Domain-Adaptive Wavelet and Improved Fuzzy Transform. A novel enhancement algorithm for degraded image using dual-domain-adaptive wavelet and improved fuzzy transform is proposed, aiming at the problem of surveillance videos degradation caused by the complex lighting conditions underground coal mine. The dual-domain filtering (DDF) is used to decompose the image into base image and detail image, and the contrast limited adaptive histogram enhancement (CLAHE) is adopted to adjust the overall brightness and contrast of the base image. The PAL fuzzy enhancement operator is improved and used to perform contrast enhancement and highlight area suppression on the reconstructed image to obtain an enhanced image. Experimental results show that the proposed algorithm can significantly improve the overall brightness and contrast of the degraded image and suppresses the noise of dust and spray and enhances the image details. Compared with the other seven algorithms, the proposed algorithm has strong robustness and is more suitable for image enhancement in different mine environments
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