本文關(guān)鍵詞： 礦井 智能監(jiān)控 圖像重構(gòu) 壓縮感知 稀疏表示　出處：《煤炭學(xué)報(bào)》2017年05期 　論文類(lèi)型：期刊論文

【摘要】：礦井智能監(jiān)控是實(shí)現(xiàn)少人或無(wú)人工作面自動(dòng)化開(kāi)采和可視化作業(yè)的重要保障。針對(duì)礦井監(jiān)控圖像易受噪聲干擾和霧塵環(huán)境等影響,采用傳統(tǒng)的基于奈奎斯特(Nyquist)采樣和壓縮方法存在分辨率低、圖像模糊和運(yùn)算時(shí)間過(guò)長(zhǎng)等問(wèn)題,根據(jù)壓縮感知和稀疏重建理論,提出了一種利用分塊壓縮感知模型和自適應(yīng)匹配追蹤均衡策略獲取礦井圖像的方法。該方法通過(guò)建立礦井圖像分塊壓縮感知模型,信源編碼過(guò)程先利用稀疏隨機(jī)矩陣對(duì)塊圖像進(jìn)行壓縮、采樣、獲得觀測(cè)值,然后使用DFT作為稀疏基進(jìn)行信號(hào)稀疏表示,最后利用一種改進(jìn)的自適應(yīng)匹配追蹤算法進(jìn)行圖像重構(gòu)實(shí)現(xiàn)解碼。研究結(jié)果表明,提出的方法在與其他算法的比較中體現(xiàn)了較好的優(yōu)越性,能有效提高礦井圖像在壓縮感知重構(gòu)階段的解碼質(zhì)量及其壓縮處理速度,具有較強(qiáng)的抗噪聲性能和魯棒性,有助于改善礦井監(jiān)控圖像的清晰度和實(shí)時(shí)處理性能。
[Abstract]:Mine intelligent monitoring is an important guarantee to realize automatic mining and visual operation of few or no man working face. The image of mine monitoring is vulnerable to noise interference and fog-dust environment and so on. The traditional Nyquist (Nyquist) sampling and compression method based on Nyquist has the problems of low resolution, blurred image and long computation time, according to the theory of compression perception and sparse reconstruction. This paper presents a method of obtaining mine image by using block compression perception model and adaptive matching tracking equalization strategy, which is based on the establishment of block compression perception model of mine image. The source coding process first uses sparse random matrix to compress the block image, samples the observed value, and then uses DFT as the sparse basis for signal sparse representation. Finally, an improved adaptive matching tracking algorithm is used for image reconstruction and decoding. The research results show that the proposed method has better advantages compared with other algorithms. It can effectively improve the decoding quality and compression processing speed of mine image in the stage of compression perception reconstruction. It has strong anti-noise performance and robustness. It is helpful to improve the clarity and real-time processing performance of mine monitoring image.
【作者單位】： 中國(guó)礦業(yè)大學(xué)(北京)機(jī)電與信息工程學(xué)院;
【基金】：國(guó)家重點(diǎn)研發(fā)計(jì)劃重點(diǎn)專(zhuān)項(xiàng)資助項(xiàng)目(2016YFC0801800) 國(guó)家自然科學(xué)基金資助項(xiàng)目(51674269) 中央高�；究蒲袠I(yè)務(wù)基金資助項(xiàng)目(2014YJ01)
【分類(lèi)號(hào)】：TD67;TP391.41
【正文快照】： 礦井智能監(jiān)控、煤巖識(shí)別和礦物探測(cè)對(duì)視頻圖像的質(zhì)量和實(shí)時(shí)性要求較高,目前,在礦井圖像壓縮方法研究領(lǐng)域,Fourier變換、小波變化及其改進(jìn)方法被國(guó)內(nèi)外學(xué)者廣泛研究。文獻(xiàn)[1]提出了小波與統(tǒng)計(jì)建模的煤巖圖像識(shí)別方法,取得了較好的識(shí)別率與實(shí)時(shí)性。文獻(xiàn)[2]提出了一種雙邊濾波的，

本文編號(hào)：1477242