本文選題：圖像去霧算法 + 加權(quán)引導(dǎo)濾波�。� 參考：《安徽大學(xué)》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(duì)光線具有吸收、散射作用,使得戶外捕捉到的圖像出現(xiàn)對(duì)比度下降,顏色衰減等降質(zhì)現(xiàn)象,導(dǎo)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(biāo)跟蹤、視頻監(jiān)控、遙感等計(jì)算機(jī)視覺(jué)方向的應(yīng)用上,霧天圖像的去霧技術(shù)研究有著重要的意義。隨著相關(guān)計(jì)算機(jī)技術(shù)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(diǎn)。去霧算法主要分為圖像增強(qiáng)和基于物理模型的圖像復(fù)原兩種。兩種類型的算法各有優(yōu)缺點(diǎn),但近年來(lái),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(guān)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(jìn)而不能真實(shí)反映景物深度的信息,導(dǎo)致去霧的不徹底;另一方面,天空等區(qū)域的透射率估計(jì)值較低,導(dǎo)致復(fù)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(lái)進(jìn)行去霧算法的研究。首先,提出了一種基于加權(quán)引導(dǎo)濾波的單幅圖像去霧算法。在引導(dǎo)濾波的基礎(chǔ)上,借助Canny算子來(lái)自適應(yīng)地設(shè)置輸入圖像的邊緣區(qū)域和平坦區(qū)域的權(quán)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(gè)特點(diǎn),采用加權(quán)引導(dǎo)濾波代替中值濾波的方法來(lái)獲取邊緣信息更加豐富的大氣面紗。同時(shí),用暗原色先驗(yàn)代替白平衡操作來(lái)估計(jì)大氣光,使得大氣光值更為準(zhǔn)確。實(shí)驗(yàn)結(jié)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(shí)地反映景物深度的信息,無(wú)論是使用中值濾波,還是加權(quán)引導(dǎo)濾波,復(fù)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(jì)值偏低,導(dǎo)致復(fù)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(biāo)區(qū)域,并適當(dāng)減小霧天圖像的各顏色通道最小值圖像相應(yīng)區(qū)域的灰度值;然后通過(guò)兩次不同引導(dǎo)圖像的加權(quán)引導(dǎo)濾波操作,得到優(yōu)化后的大氣面紗,同時(shí)根據(jù)暗原色先驗(yàn)估計(jì)大氣光;最后根據(jù)大氣散射模型反演得到復(fù)原圖像。實(shí)驗(yàn)結(jié)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(shí)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(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 楚君;王華彬;陶亮;周健;;基于引導(dǎo)濾波器的單幅霧天圖像復(fù)原算法[J];計(jì)算機(jī)工程與應(yīng)用;2015年21期

2 郭t，

本文編號(hào)：1908663