發(fā)布時間：2018-03-11 07:01

  本文選題：圖像去霧　切入點：顏色衰減先驗　出處：《太原理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：人類的生產生活對自然環(huán)境造成了嚴重影響,導致霧霾天氣出現(xiàn)的日漸頻繁。霧天氣條件下拍攝的照片模糊不清,對各類視頻監(jiān)控系統(tǒng)的使用帶來了不便,因此對圖像進行去霧處理,恢復出清晰的圖像,是現(xiàn)在的研究熱點,也是很有實際價值的課題。根據(jù)顏色衰減先驗知識進行去霧是近兩年提出的一個新的去霧方法,該方法快速有效,極大的提高了去霧速度,具有推廣價值。筆者在實驗過程中發(fā)現(xiàn),該算法中大氣散射系數(shù)的選擇對最終的去霧結果影響很大,需要進行人工調整。而且,顏色衰減先驗知識,在霧濃的區(qū)域失效,因此對霧濃的圖像處理效果不佳。本文針對這兩個問題進行了改進。本文在顏色衰減先驗知識的基礎上,提出了基于小波融合的圖像去霧算法。首先,建立了透射率關于圖像亮度、飽和度的線性模型。其次,選用400幅圖像及其準確的景深信息用于訓練樣本的生成,并將大氣散射系數(shù)的概率分布直接融入到訓練樣本中,保證了訓練樣本生成的準確性,隨后采用機器學習中的監(jiān)督學習算法估計出圖像透射率,然后用小波融合算法將估計出的透射率與圖像灰度圖的反轉圖像融合。最后,用細化后的透射率信息對圖像進行去霧處理。直接對透射率進行建模的過程,避免了大氣散射系數(shù)的選擇,用圖像灰度圖的反轉圖對透射率進行細化,提高了透射率的準確性,最終提高了對霧濃圖像的處理效果。實驗結果證明本文算法可行有效。此外文章采用通用的去霧圖像質量評價方法將本算法與先進的去霧算法進行了比較。實驗結果表明,本文算法提高了透射率的準確性,改善了濃霧區(qū)域的恢復效果。具有適用性好,計算復雜度低的特點。本文的主要貢獻為以下幾點:(1)建立了透射率關于圖像亮度飽和度的線性模型,避免了去霧過程中對大氣散射系數(shù)的人工選擇。(2)得到大氣散射系數(shù)的分布直方圖,并將其代入訓練樣本中,從而獲得準確度高的訓練樣本,保證訓練出模型的可靠性。(3)提出圖像灰度圖的反轉圖能夠作為透射率對圖像進行去霧處理,尤其對霧濃的圖像,處理效果較佳。(4)利用小波算法將估計出的透射率與圖像灰度圖的反轉圖融合,提高透射率的準確性,保證最終的去霧效果。
[Abstract]:The production and life of human beings have caused a serious impact on the natural environment, resulting in the increasing frequency of haze weather. The photographs taken under the fog weather conditions are blurred, which brings inconvenience to the use of all kinds of video surveillance systems. Therefore, to defog the image and restore the clear image is a hot research topic and a very valuable subject. According to the prior knowledge of color attenuation, de-fogging is a new de-fogging method proposed in the last two years. This method is fast and effective, greatly improves the speed of de-fogging, and is worth popularizing. In the course of experiment, the author finds that the selection of atmospheric scattering coefficient in this algorithm has a great influence on the final de-fogging result and needs to be adjusted manually. The prior knowledge of color attenuation is invalid in the region of fog concentration, so the image processing effect of fog concentration is not good. This paper improves these two problems, and based on the prior knowledge of color attenuation, An image de-fogging algorithm based on wavelet fusion is proposed. Firstly, a linear model of transmittance for image brightness and saturation is established. Secondly, 400 images and their accurate depth of field information are used to generate training samples. The probability distribution of atmospheric scattering coefficient is directly incorporated into the training sample to ensure the accuracy of the training sample generation. Then the image transmittance is estimated by the supervised learning algorithm in machine learning. Then wavelet fusion algorithm is used to fuse the estimated transmittance with the inverse image of the gray image. Finally, the thinned transmittance information is used to defog the image. The atmospheric scattering coefficient is avoided, and the transmissivity is thinned by the inverse image of the image grayscale image, which improves the accuracy of the transmittance. Finally, the processing effect of fog concentration image is improved. The experimental results show that the proposed algorithm is feasible and effective. In addition, this algorithm is compared with the advanced de-fogging algorithm by using the general evaluation method of de-fogging image quality. The experimental results show that, The algorithm improves the accuracy of transmittance and improves the recovery effect of dense fog region. The main contributions of this paper are as follows: 1) A linear model of transmittance for image luminance saturation is established. The distribution histogram of atmospheric scattering coefficient is obtained by avoiding the artificial selection of atmospheric scattering coefficient in the de-fogging process, and the distribution histogram of atmospheric scattering coefficient is added to the training sample to obtain the training sample with high accuracy. To ensure the reliability of the model, it is proposed that the inverse image of gray image can be used as transmittance to defog the image, especially for the dense fog image. The wavelet algorithm is used to fuse the estimated transmittance with the inverse image of gray image to improve the accuracy of transmittance and ensure the final effect of fog removal.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關期刊論文 前10條

1 劉樹華;繆育聰;;京津冀霧霾成因與治理[J];中國經(jīng)濟報告;2017年02期

2 王力冠;馮瑞;;基于霍夫森林和半監(jiān)督學習的圖像分類[J];計算機工程與應用;2016年20期

3 齊妙;馬書一;王婷;管慶吉;;基于小波融合的單幅圖像去霧方法[J];東北師大學報(自然科學版);2016年02期

4 孫曉曉;楊峰;;基于小波變換融合技術的去霧方法[J];山東師范大學學報(自然科學版);2016年02期

5 董蕊;王志巍;劉淑娟;張有會;趙曄;;基于統(tǒng)計直方圖的單幅圖像去霧算法[J];計算機應用與軟件;2016年05期

6 麥嘉銘;王美華;梁云;蔡瑞初;;特征學習的單幅圖像去霧算法[J];中國圖象圖形學報;2016年04期

7 付輝;吳斌;韓東軒;黃陽強;;基于大氣物理模型的快速視覺優(yōu)化去霧算法[J];計算機應用;2015年11期

8 繆育聰;鄭亦佳;王姝;劉樹華;;京津冀地區(qū)霾成因機制研究進展與展望[J];氣候與環(huán)境研究;2015年03期

9 雷琴;施朝健;陳婷婷;;基于天空區(qū)域分割的單幅海面圖像去霧方法[J];計算機工程;2015年05期

10 李加元;胡慶武;艾明耀;嚴俊;;結合天空識別和暗通道原理的圖像去霧[J];中國圖象圖形學報;2015年04期

相關碩士學位論文 前4條

1 張集鈞;石家莊市霧霾治理政策分析及建議[D];河北師范大學;2015年

2 唐美玲;單幅圖像去霧算法的研究與應用[D];湖南大學;2014年

3 趙瑩;基于單幅圖像的去霧算法研究[D];天津大學;2009年

4 李鵬;薄塵霧退化圖像的處理研究[D];南京理工大學;2005年

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本文編號：1597068