Research on coal mine underground image recognition technology based on homomorphic filtering method

Visual SLAM technology is widely used in underground search and rescue work, and the quality of image collected by robot directly determines the quality of image composition. At present, due to the influence of dust and light source con-ditions in underground coal mine, the enhancement effect of underground image needs to be improved. At present, the coal mine monitoring image enhancement effect needs to be improved due to the influence of dust and light source conditions in the coal mine.In order to solve this problem, this paper puts forward a HSV space combined with Adaptive Gamma Correcti-on with Weighting Distribution (AGCWD) homomorphic filtering method.Firstly, to solve the problem of over-enhancement of the highlight and shadow areas existing in the classical homomorphic filtering algorithm, the AGCWD algorithm is used to carry out adaptive gamma correction for the probability density of the V component in HSV space, and the new probability distribution is non-linearly mapped to improve the applicability of the homomorphic filtering to the high light and shadow ar-eas.Then single-parameter homomorphic filter is used for processing to alleviate the problem of difficult parameter selection c-aused by multiple parameters.In order to preserve the detail of the image, and then the results of single parameter after the homomorphic filtering to carry on the Contrast Limited Histograme Equalization(CLAHE);Finally, HSV inverse transformation is carried out to obtain the image in RGB space, and image enhancement is completed.By the improved homomorphic filterin-g algorithm, CLAHE algorithm and classical homomorphic filtering algorithm proposed in this experiment, the result image mean, standard deviation, peak signal-to-noise ratio (PSNR), information entropy and other indicators are evaluated.Compared with the CLAHE algorithm, the improved homomorphic filtering algorithm is improved by 65.29%, 21.58%, 17.03% and 5.18% respectively, and compared with the classical homomorphic filtering algorithm, it is improved by 52.07%, 40.73%, 36.23% and 8.96% respectively.The experimental data show that the improved homomorphic filtering algorithm can enhance the b-rightness and contrast of the image and keep the detail information of the image. At the same time, the overenhancement p-henomenon of classical homomorphic filtering on the image with large gap between light and dark is suppressed to a certainn extent. © 2023 Meitan Kexue Jishu/Coal Science and Technology (Peking).