本文選題：圖像去霧算法 + 加權引導濾波��； 參考：《安徽大學》2017年碩士論文

【摘要】：霧霾天氣下,由于大氣中存在的懸浮顆粒對光線具有吸收、散射作用,使得戶外捕捉到的圖像出現(xiàn)對比度下降,顏色衰減等降質現(xiàn)象,導致物體特征難以辨別,圖像的觀賞性降低,影響圖像的后續(xù)處理。因此,在目標跟蹤、視頻監(jiān)控、遙感等計算機視覺方向的應用上,霧天圖像的去霧技術研究有著重要的意義。隨著相關計算機技術的日益發(fā)展與趨于成熟,霧天圖像的去霧研究也成為了廣大研究人員研究的熱點。去霧算法主要分為圖像增強和基于物理模型的圖像復原兩種。兩種類型的算法各有優(yōu)缺點,但近年來,基于物理模型的的去霧算法更為普遍。其中,基于中值濾波的快速去霧算法因其快速的去霧處理速度和良好的去霧效果而受到關注。但是,一方面,由中值濾波得到的大氣面紗丟失了很多圖像的邊緣信息,進而不能真實反映景物深度的信息,導致去霧的不徹底;另一方面,天空等區(qū)域的透射率估計值較低,導致復原圖像中出現(xiàn)色彩失真和噪聲。為此,本文主要圍繞大氣面紗和透射率兩方面來進行去霧算法的研究。首先,提出了一種基于加權引導濾波的單幅圖像去霧算法。在引導濾波的基礎上,借助Canny算子來自適應地設置輸入圖像的邊緣區(qū)域和平坦區(qū)域的權值,使得輸出圖像保留了更多的邊緣信息,平滑了更多的噪聲紋理信息。根據(jù)這個特點,采用加權引導濾波代替中值濾波的方法來獲取邊緣信息更加豐富的大氣面紗。同時,用暗原色先驗代替白平衡操作來估計大氣光,使得大氣光值更為準確。實驗結果表明,本算法有著較快的去霧處理速度,且邊緣處的去霧更為徹底。其次,提出了一種基于大氣面紗優(yōu)化和透射率修正的單幅圖像去霧算法。一方面,由于大氣面紗的求解算法引入了噪聲紋理信息,使得大氣面紗并不能真實地反映景物深度的信息,無論是使用中值濾波,還是加權引導濾波,復原圖像都存在去霧的不徹底。另一方面,天空等區(qū)域由于透射率的估計值偏低,導致復原圖像存在噪聲和色彩失真。為此,首先根據(jù)閾值分割得到天空等目標區(qū)域,并適當減小霧天圖像的各顏色通道最小值圖像相應區(qū)域的灰度值;然后通過兩次不同引導圖像的加權引導濾波操作,得到優(yōu)化后的大氣面紗,同時根據(jù)暗原色先驗估計大氣光;最后根據(jù)大氣散射模型反演得到復原圖像。實驗結果表明,本算法在景物深度的不連續(xù)區(qū)域有著更為徹底的去霧效果,同時天空區(qū)域避免了噪聲、色彩失真,與大氣光相似的白色區(qū)域避免了顏色偏暗。
[Abstract]:In haze weather, because of the absorption and scattering of light by suspended particles in the atmosphere, the images captured outdoors are degraded in contrast and color attenuation, which leads to the difficulty of distinguishing the characteristics of objects. Image viewing is reduced, which affects the subsequent processing of the image. Therefore, in the applications of target tracking, video surveillance, remote sensing and other computer vision directions, the research on fog removal technology of fog images is of great significance. With the development and maturity of computer technology, the research of fog image defog has become a hot topic for researchers. The de-fogging algorithm is mainly divided into two kinds: image enhancement and image restoration based on physical model. The two kinds of algorithms have their own advantages and disadvantages, but in recent years, physical model-based de-fogging algorithms are more common. Among them, the fast de-fogging algorithm based on median filter is concerned because of its fast de-fogging speed and good de-fogging effect. However, on the one hand, the atmospheric veil obtained by the median filter has lost the edge information of many images, which can not truly reflect the depth of the scene, resulting in incomplete fog removal. On the other hand, the estimated transmittance of the sky and other regions is lower. Color distortion and noise appear in the restored image. Therefore, this paper mainly focuses on the atmospheric veil and transmittance to study the fog removal algorithm. Firstly, a single image de-fogging algorithm based on weighted guided filter is proposed. On the basis of guided filtering, the Canny operator is used to set the weights of the edge region and flat region of the input image adaptively, which makes the output image retain more edge information and smooth more noise texture information. According to this characteristic, the weighted guided filter is used instead of the median filter to obtain the atmospheric veil with richer edge information. At the same time, the dark primary color is used instead of the white balance operation to estimate atmospheric light, which makes the atmospheric light more accurate. The experimental results show that the proposed algorithm has a faster de-fogging speed and a more thorough de-fogging at the edge. Secondly, a single image de-fogging algorithm based on atmospheric veil optimization and transmittance correction is proposed. On the one hand, because the noise texture information is introduced into the solution algorithm of atmospheric veil, the atmospheric veil can not truly reflect the depth of the scene, whether using median filter or weighted guided filter. Restoration images are not completely foggy. On the other hand, because the estimated transmittance of the sky and other regions is low, there is noise and color distortion in the reconstructed image. Therefore, the sky and other target regions are segmented according to the threshold value, and the gray value of the corresponding region of the minimum value of each color channel of the fog image is appropriately reduced, and then the weighted guided filtering operation of two different guided images is carried out. The optimized atmospheric veil is obtained, and the atmospheric light is estimated prior to the dark primary color. Finally, the reconstructed image is obtained by inversion of the atmospheric scattering model. The experimental results show that the algorithm has a more thorough effect of removing fog in the discontinuous region of the depth of the scene, at the same time, the noise and color distortion are avoided in the sky region, and the white area similar to the atmospheric light avoids the dark color.
【學位授予單位】：安徽大學
【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3條

1 楚君;王華彬;陶亮;周健;;基于引導濾波器的單幅霧天圖像復原算法[J];計算機工程與應用;2015年21期

2 郭t，

本文編號：1908663