發(fā)布時間：2018-04-29 01:21

  本文選題：車行視程 + 大氣光估值�。� 參考：《工程科學(xué)與技術(shù)》2017年03期

【摘要】：交通場景中的視頻圖像去霧處理,是一個實時性極強的不確定反問題,針對霧霾天氣下車載視頻圖像退化嚴(yán)重的現(xiàn)象,分析了交通環(huán)境中霧氣濃度對車前物景可視度的關(guān)系,提出了大氣能見度與車行視覺距離之間的關(guān)系模型,討論了降質(zhì)圖像增強的理論與方法,建立了霧霾條件下車行可視距離的線性回歸公式和基于大氣能見度的透射率快速估值模型。同時,研究了霧霾物像暗通道的基本特征,提出了利用引導(dǎo)濾波對圖像實現(xiàn)邊緣平滑、細節(jié)增強以及利用盒式濾波保持邊緣信息,降低時間復(fù)雜度;直接利用灰度圖像獲取大氣光像素矩陣的估值方法,建立了基于大氣光的快速估計模型,解決了暗原色先驗理論方法的"非天空區(qū)"假設(shè)及其時間復(fù)雜度難以適應(yīng)車載視頻圖像處理的問題。根據(jù)上述提出的透射率估計方法和天空光的估值模型,本文提出了一種魯棒性好,實時性強的霧霾視頻圖像去霧的新算法,并完成了基于該方法的視頻圖像恢復(fù)處理流程設(shè)計,構(gòu)造了車載霧霾視頻圖像恢復(fù)處理的綜合驗證平臺,通過信息熵和圖像邊緣檢測的方法,對本文提出的方法與目前已有的幾種流行的去霧方法進行了圖像恢復(fù)質(zhì)量的比較,結(jié)果表明,本文提出的方法在反映圖像細節(jié)和清晰化等方面都取得了良好的處理效果。
[Abstract]:The de-fogging of video images in traffic scene is a highly real-time uncertain inverse problem. In view of the serious degradation of vehicle video images in haze weather, the relationship between fog concentration in traffic environment and the viewability of vehicle front scene is analyzed. The relationship model between visibility and visual distance is proposed, and the theory and method of image enhancement are discussed. A linear regression formula for the visible distance of a vehicle under haze and a fast transmissivity estimation model based on atmospheric visibility are established. At the same time, the basic characteristics of the dark channel of haze image are studied, and the image edge smoothing, detail enhancement and the use of box filter to keep edge information are proposed to reduce the time complexity. A fast estimation model based on atmospheric light is established by directly using gray image to obtain the estimation method of atmospheric light pixel matrix. The "non-sky region" hypothesis of dark priori theory and its time complexity are difficult to adapt to vehicle video image processing. According to the above proposed transmittance estimation method and the sky light estimation model, this paper proposes a new algorithm for removing fog from haze video images with good robustness and real time performance, and designs the video image restoration processing flow based on this method. A comprehensive verification platform for vehicle haze video image recovery processing is constructed. By using information entropy and image edge detection methods, the image restoration quality is compared with several popular de-fogging methods in this paper. The results show that the method presented in this paper has achieved good processing results in terms of image detail and clarity.
【作者單位】： 四川大學(xué)制造科學(xué)與工程學(xué)院;
【基金】：四川省科技支撐計劃資助項目(2014Z0007;2010GZ0171)
【分類號】：TP391.41;U463.6

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本文編號：1817758