本文選題：井下視頻監(jiān)控系統(tǒng) + 中值濾波��； 參考：《金屬礦山》2017年12期

【摘要】：井下視頻監(jiān)控系統(tǒng)的應(yīng)用對于實時獲取井下生產(chǎn)進(jìn)度、機(jī)電設(shè)備運(yùn)行狀況等信息,及時有效開展井下應(yīng)急救援工作發(fā)揮了重要作用,但井下光照不均勻、空氣中大量粉塵導(dǎo)致監(jiān)控系統(tǒng)獲取的圖像較模糊,影響了對井下各類信息進(jìn)行有效采集和分析。以新元礦井下視頻監(jiān)控系統(tǒng)為例,將中值濾波算法與小波軟閾值去噪模型相結(jié)合,提出了一種井下視頻圖像濾波方法。該方法預(yù)先對原始視頻圖像進(jìn)行3層小波變換,對獲取的低頻小波系數(shù)和高頻小波系數(shù)分別進(jìn)行逆變換,得到3幅低頻圖像和3幅高頻圖像;針對低頻圖像,采用融合噪聲判別準(zhǔn)則的改進(jìn)中值濾波算法進(jìn)行去噪;對于高頻圖像采用改進(jìn)型小波軟閾值去噪模型進(jìn)行處理。在此基礎(chǔ)上,將濾波后的低頻和高頻圖像進(jìn)行融合,實現(xiàn)對視頻圖像的高效濾波。采用該礦井下兩幅視頻圖像對所提方法進(jìn)行試驗,并與中值濾波算法、小波硬閾值去噪模型、小波軟閾值去噪模型進(jìn)行對比分析,結(jié)果表明,所提算法處理后的圖像清晰度明顯優(yōu)于其余3類算法,且該方法耗時較短,適合于高效處理井下視頻監(jiān)控圖像。
[Abstract]:The application of underground video surveillance system plays an important role in obtaining the information of downhole production progress, electromechanical equipment running condition, and carrying out underground emergency rescue work in time and effectively, but the underground lighting is not uniform.A large amount of dust in the air leads to blurred images obtained by the monitoring system, which affects the effective collection and analysis of all kinds of underground information.Taking the video monitoring system of Xinyuan mine as an example, a downhole video image filtering method is proposed by combining the median filtering algorithm with the wavelet soft threshold denoising model.In this method, the original video image is pre-processed with three-layer wavelet transform, and the obtained low-frequency wavelet coefficients and high-frequency wavelet coefficients are inversely transformed respectively to obtain three low-frequency images and three high-frequency images.The improved median filtering algorithm based on fusion noise criterion is used to remove noise, and the improved wavelet soft threshold denoising model is used to process high frequency images.On this basis, the filtered low-frequency and high-frequency images are fused to realize the efficient filtering of video images.The proposed method is tested by two video images under the mine and compared with median filtering algorithm, wavelet hard threshold denoising model and wavelet soft threshold denoising model. The results show that,The image sharpness of the proposed algorithm is obviously better than that of the other three kinds of algorithms, and the time consuming of the proposed algorithm is relatively short, so it is suitable for efficient processing of underground video surveillance images.
【作者單位】： 江蘇海事職業(yè)技術(shù)學(xué)院信息工程學(xué)院;
【分類號】：TD76;TP391.41

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