本文選題：暗通道 + 局部大氣光　； 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：隨著科技的進步,視頻監(jiān)控系統(tǒng)已得到廣泛應(yīng)用,為現(xiàn)代社會的生產(chǎn)和生活帶來方便并提供安全保障。例如,銀行、大型商場、公司等場所的安保監(jiān)控系統(tǒng),可以了解室內(nèi)情況,顧客行為等,為顧客的人身和財產(chǎn)安全作保障;在交通監(jiān)控方面,監(jiān)控范圍廣,交管部門能夠在第一時間發(fā)現(xiàn)問題,解決問題。而當(dāng)戶外天氣被霧氣籠罩時,人的視覺會出現(xiàn)朦朧感,攝像監(jiān)控設(shè)備也會受到天氣的影響,所拍攝到的畫面不清晰。這樣所得到的圖像會使圖像信息特征提取困難、圖像使用不精確,從而降低圖像的使用價值,也會直接影響到視頻監(jiān)控系統(tǒng)的實用性。交通道路特別是高速公路上在霧天更容易發(fā)生交通事故,交通監(jiān)控顯示的畫面模糊,給交管部門及時掌握交通狀況帶來很大不便。因此,對霧天條件下所拍攝的有霧圖像實施去霧處理,提高圖像質(zhì)量是很有必要的。He等人的暗通道先驗去霧算法是去霧領(lǐng)域中的經(jīng)典算法,但該算法如果濾波窗口選擇太小,則會導(dǎo)致暗通道先驗理論的失效,窗口過大,會導(dǎo)致有霧圖像的暗通道在邊緣區(qū)域評估過小,透射率圖出現(xiàn)誤差;大氣光的粗略估計,也會導(dǎo)致恢復(fù)圖像細節(jié)不突出�；诎低ǖ老闰炈惴ㄖ袨V波窗口選擇的不足和大氣光粗略評估所導(dǎo)致的細節(jié)不突出的問題,本文的主要工作有:(1)本文提出形態(tài)學(xué)暗通道去霧算法,修正暗通道窗口過大導(dǎo)致的過渡腐蝕現(xiàn)象。(2)本文采用形態(tài)學(xué)亮通道算法確定局部大氣光的算法,修正暗通道窗口過小則會導(dǎo)致的暗通道先驗理論的失效的現(xiàn)象,恢復(fù)圖像的天空區(qū)域細節(jié),改善圖像近景視覺效果。對于道路場景圖像的處理,目前,車輛視覺導(dǎo)航已廣泛應(yīng)用于智能交通、安全輔助駕駛等領(lǐng)域。道路交通信息,如道路車道線、交通標志牌等信息是車輛視覺導(dǎo)航系統(tǒng)發(fā)揮作用的前提和基礎(chǔ)。本文對去霧算法的應(yīng)用主要有以下幾個方面:(1)去霧算法在霧天環(huán)境下交通場景圖像中的應(yīng)用。(2)去霧算法在道路車道線的特征提取中的應(yīng)用。(3)去霧算法在車牌信息監(jiān)測的應(yīng)用。通過對幾種經(jīng)典去霧算法進行分析以及視覺效果的對比,發(fā)現(xiàn)本文算法具有更好的實時性。
[Abstract]:With the development of science and technology, video surveillance system has been widely used, which brings convenience and security for the production and life of modern society. For example, security monitoring systems in banks, large shopping malls, companies, and other places can understand indoor conditions, customer behavior, and so on, so as to ensure the personal and property safety of customers. In terms of traffic monitoring, the scope of monitoring is wide. The traffic control department can find the problem and solve the problem in the first time. When the outdoor weather is shrouded in fog, people's vision will appear hazy, camera monitoring equipment will also be affected by the weather, and the picture taken is not clear. The obtained image will make it difficult to extract the features of the image information, and the use of the image is imprecise, thus reducing the use value of the image and directly affecting the practicability of the video surveillance system. Traffic accidents are more likely to occur in foggy days on traffic roads, especially on expressways, and the pictures of traffic monitoring and display are blurred, which brings great inconvenience to traffic control departments in time to grasp traffic conditions. Therefore, it is necessary to defog the fogged images taken under fog conditions and improve the image quality. The prior dark channel de-fogging algorithm proposed by he et al is a classical algorithm in the field of fog removal, but if the filter window is too small, the filter window is too small. It will lead to the failure of the prior theory of dark channel, too large window, so that the dark channel with fog image will be evaluated too small in the edge region, and the transmittance map will appear error, and the rough estimation of atmospheric light will also lead to the inconspicuous details of the restoration image. Based on the deficiency of filtering window selection in dark channel priori algorithm and the lack of detail due to rough assessment of atmospheric light, the main work of this paper is: 1) in this paper, we propose a morphological dark channel de-fogging algorithm. In this paper, the morphological bright channel algorithm is used to determine the local atmospheric light, and the failure of the dark channel priori theory will be corrected if the dark channel window is too small. Restore the image of the sky area details, improve the image close-range visual effect. For road scene image processing, vehicle visual navigation has been widely used in the fields of intelligent transportation, safety assisted driving and so on. Road traffic information, such as road lane, traffic signs and so on, is the premise and foundation of vehicle visual navigation system. In this paper, the application of de-fogging algorithm is as follows: 1) the application of de-fogging algorithm in traffic scene image in fog environment.) the application of de-fogging algorithm in feature extraction of road lane line.) the application of de-fogging algorithm in license plate information monitoring. By analyzing several classical de-fogging algorithms and comparing the visual effects, it is found that the proposed algorithm has better real-time performance.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TN948.6

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本文編號：1800954