基于先驗知識的圖像去霧算法
發(fā)布時間：2018-11-19 07:17
【摘要】：霧霾天氣下懸浮在空氣中的大氣顆粒物對光線的傳播產(chǎn)生了不利影響,使得成像設(shè)備獲取的圖像能見度低、對比度下降,給圖像分割、目標(biāo)跟蹤、行為檢測等后續(xù)計算機(jī)視覺任務(wù)造成了極大不便,直接影響到現(xiàn)有戶外成像系統(tǒng)(如安防監(jiān)控系統(tǒng)等)的正常工作,給人們的生活造成了巨大的安全隱患。因此,研究如何提高霧天降質(zhì)圖像復(fù)原結(jié)果質(zhì)量、降低霧霾天氣對現(xiàn)有戶外成像系統(tǒng)的不利影響有著十分重要的現(xiàn)實意義。本文從霧霾天氣的特點出發(fā),詳細(xì)分析了大氣顆粒物對圖像成像過程的影響及霧天降質(zhì)圖像的退化過程。通過分析研究現(xiàn)有的圖像去霧算法,發(fā)現(xiàn)其中存在的不足及可改進(jìn)之處。本文首先介紹了大氣光散射模型,并通過理論推導(dǎo)證明了其中存在的不足并提出了有效的改進(jìn)方法,同時還針對現(xiàn)有去霧算法在獲取大氣光值時存在誤差過大這一問題進(jìn)行了完善與改進(jìn),做出了一些有意義的實際工作。概括而言,本論文的主要工作及創(chuàng)新主要集中在如下幾個方面:1)針對暗原色先驗去霧算法結(jié)果色彩失真的問題,提出了一種對各顏色通道分別計算透射率的改進(jìn)方法。算法利用了大氣介質(zhì)對各顏色可見光透射率不同的先驗知識。首先依據(jù)比爾郎伯定律分析入射光頻率對各顏色通道透射率的影響,推導(dǎo)出各通道透射率之間的比例關(guān)系,然后采用先對圖像進(jìn)行降采樣預(yù)處理獲取細(xì)化透射率之后再恢復(fù)原尺寸的方法提高算法運行效率,最后通過比例關(guān)系獲取所有顏色通道上的透射率,并在各通道上分別使用對應(yīng)的透射率恢復(fù)圖像。實驗結(jié)果表明,改進(jìn)后的圖像去霧算法與現(xiàn)有算法的結(jié)果相比,去霧結(jié)果圖像色彩更加自然,消除了現(xiàn)有算法色彩飽和度偏高的缺點,且算法運算效率大幅提高。2)現(xiàn)有去霧算法在估算大氣光時過于粗略導(dǎo)致估算結(jié)果誤差較大,造成圖像復(fù)原結(jié)果常常存在色彩失真的問題。針對這一問題,本文提出了一種基于聚類統(tǒng)計的大氣光估算方法,主要利用了光源點處大氣光樣本點分布更加密集的先驗知識。算法首先在原圖中選取部分可能的大氣光源點,通過無監(jiān)督聚類算法對這部分大氣光源點進(jìn)行聚類,聚類出若干個備選大氣光源點點簇,再對各點簇中所含樣本點個數(shù)進(jìn)行統(tǒng)計,選取包含點數(shù)最多的點簇求解大氣光。使用點簇中各大氣光樣本點的亮度均值向量作為大氣光的估算值,同時以各點簇的幾何中心作為大氣光所處位置。實驗結(jié)果表明,統(tǒng)計聚類方式估計的大氣光亮度向量和光源位置都更加準(zhǔn)確,這使得圖像復(fù)原結(jié)果在主觀視覺上看起來更加自然,同時也較大地提升了各類圖像質(zhì)量客觀評價指標(biāo)。3)現(xiàn)有方法采用固定數(shù)量的大氣光樣本點進(jìn)行聚類,并以包含候選點最多的點簇統(tǒng)計估算大氣光值。由于樣本點較少,導(dǎo)致估算的大氣光值在統(tǒng)計意義上誤差較大。為了解決這一問題,本文采用閾值劃分的方式選取大氣光樣本點,以此提高大氣光樣本點數(shù)量,同時采用蟻群算法聚類大氣光點簇,提高大氣光值估算結(jié)果的準(zhǔn)確度。為了提高算法的計算效率,本文先使用K均值算法對大氣光候選點進(jìn)行初步聚類,再使用蟻群算法改良聚類結(jié)果。實驗結(jié)果證明,使用該算法估算的大氣光值能使去霧結(jié)果看起來更加自然,且能進(jìn)一步改善去霧結(jié)果的圖像質(zhì)量評價指標(biāo)。4)現(xiàn)有去霧算法通常假定大氣光值全局恒定,而實際場景中各區(qū)域的大氣光值分布不均,利用這一先驗知識,本文提出了基于高斯分布的大氣光估計算法。算法使用閾值劃分的方式選取候選點以增加初始樣本點數(shù)量,同時引入聚類算法對原算法所得光源點點簇進(jìn)行合并以提高單個點簇所含樣本點個數(shù)。使用比例閾值過濾掉不合理的點簇,同時將各點簇視為單獨光源單獨計算其對周圍像素的影響,并通過二維高斯分布函數(shù)對此進(jìn)行建模,最后生成位置相關(guān)的大氣光圖代替全局大氣光。實驗結(jié)果表明,使用高斯分布大氣光圖復(fù)原的結(jié)果在主觀視覺上相對于原算法看起來更加自然,且在圖像質(zhì)量評價指標(biāo)上也得到改善。
[Abstract]:The air particles suspended in the air in the haze weather have adverse effects on the propagation of light, so that the image visibility acquired by the imaging device is low, the contrast is reduced, and the following computer visual tasks such as image segmentation, target tracking, behavior detection and the like are greatly inconvenient, the normal operation of an existing outdoor imaging system (such as a security monitoring system and the like) is directly affected, and a great potential safety hazard is caused to people's life. Therefore, it is of great practical significance to study how to improve the quality of the image restoration of the fog and reduce the adverse effect of the haze weather on the existing outdoor imaging system. In this paper, the effects of the atmospheric particulate matter on the image forming process and the degradation process of the fog-based image are analyzed in detail from the characteristics of the haze weather. By analyzing the existing image de-fog algorithm, it is found that the existing image de-fog algorithm can be improved. In this paper, the atmospheric light scattering model is introduced, and the shortcomings in the model are proved by the theory, and the effective improvement method is put forward. At the same time, the problem of the existing de-fog algorithm in obtaining the atmospheric light value is improved and improved. Some meaningful work has been made. In general, the main work and innovation of this thesis are mainly focused on the following aspects: 1) For the problem of color distortion of the dark primary-color prior de-fog algorithm, an improved method for calculating the transmittance for each color channel is proposed. the algorithm utilizes the prior knowledge that the atmospheric medium has different visible light transmittance for each color. firstly, the influence of the incident light frequency on the transmittance of each color channel is analyzed according to the Biberman's law, the proportion relation between the transmittance of each channel is deduced, and finally, the transmittance on all color channels is obtained through the proportional relation, and corresponding transmittance recovery images are respectively used on each channel. The experimental results show that the improved image defogging algorithm is more natural than the results of the existing algorithm, and the disadvantage of high color saturation of the existing algorithm is eliminated, and 2) the existing defogging algorithm is too rough to estimate the atmospheric light, and the error of the estimation result is large, so that the image restoration result is often a problem of color distortion. In order to solve this problem, a method for estimating the atmospheric light based on the statistics of the light source is proposed, which mainly uses the prior knowledge of the distribution of the atmospheric light sample at the light source point. The method comprises the following steps of: firstly, selecting a part of the possible atmospheric light source points in the original drawing, carrying out the clustering on the part of the atmospheric light source point by a non-supervised clustering algorithm, clustering a plurality of alternative atmospheric light source spots, and counting the number of sample points contained in each cluster, the cluster of points with the largest number of points is selected to solve the atmospheric light. the luminance average vector of each atmospheric light sample point in the cluster is used as the estimated value of the atmospheric light, and the geometric center of each cluster is used as the position of the atmospheric light. The experimental results show that the light intensity vector and the position of the light source are more accurate, which makes the image restoration appear more natural in the subjective vision. in that prior method, a fixed numb of atmospheric light sample points are adopt for clustering, and the atmospheric light value is estimated at the most point cluster containing the candidate points. As the sample point is small, the estimated atmospheric light value is in a statistically significant error. In order to solve this problem, the method of threshold division is used to select the sample point of the atmospheric light, so as to improve the number of the sample points of the atmospheric light, and the clustering of the atmospheric light spot by the ant colony algorithm is used to improve the accuracy of the estimation result of the atmospheric light value. In order to improve the calculation efficiency of the algorithm, the paper first uses the K-means algorithm to carry out the preliminary clustering of the atmospheric light candidate points, and then uses the ant colony algorithm to improve the clustering result. the experimental results show that the atmospheric light value estimated by the algorithm can make the defogging result look more natural and can further improve the image quality evaluation index of the defogging result. In the actual scene, the distribution of the atmospheric light in each area is not uniform, and this prior knowledge is used, and an atmospheric light estimation algorithm based on the Gaussian distribution is proposed in this paper. the algorithm selects the candidate points in a way of threshold division to increase the number of initial sample points, and simultaneously introduces a clustering algorithm to combine the light source spot clusters obtained by the original algorithm so as to improve the number of sample points contained in a single dot cluster. Unreasonable point clusters are filtered out by using a proportional threshold, and each cluster is considered as a single light source to separately calculate the effect on the surrounding pixels, and the two-dimensional Gaussian distribution function is used for modeling the cluster, and finally, a position-related atmospheric light diagram is generated in place of the global atmospheric light. The experimental results show that the result of using the Gaussian distribution atmospheric photo-graph is more natural in the subjective vision than the original algorithm, and is also improved in the image quality evaluation index.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
[Abstract]:The air particles suspended in the air in the haze weather have adverse effects on the propagation of light, so that the image visibility acquired by the imaging device is low, the contrast is reduced, and the following computer visual tasks such as image segmentation, target tracking, behavior detection and the like are greatly inconvenient, the normal operation of an existing outdoor imaging system (such as a security monitoring system and the like) is directly affected, and a great potential safety hazard is caused to people's life. Therefore, it is of great practical significance to study how to improve the quality of the image restoration of the fog and reduce the adverse effect of the haze weather on the existing outdoor imaging system. In this paper, the effects of the atmospheric particulate matter on the image forming process and the degradation process of the fog-based image are analyzed in detail from the characteristics of the haze weather. By analyzing the existing image de-fog algorithm, it is found that the existing image de-fog algorithm can be improved. In this paper, the atmospheric light scattering model is introduced, and the shortcomings in the model are proved by the theory, and the effective improvement method is put forward. At the same time, the problem of the existing de-fog algorithm in obtaining the atmospheric light value is improved and improved. Some meaningful work has been made. In general, the main work and innovation of this thesis are mainly focused on the following aspects: 1) For the problem of color distortion of the dark primary-color prior de-fog algorithm, an improved method for calculating the transmittance for each color channel is proposed. the algorithm utilizes the prior knowledge that the atmospheric medium has different visible light transmittance for each color. firstly, the influence of the incident light frequency on the transmittance of each color channel is analyzed according to the Biberman's law, the proportion relation between the transmittance of each channel is deduced, and finally, the transmittance on all color channels is obtained through the proportional relation, and corresponding transmittance recovery images are respectively used on each channel. The experimental results show that the improved image defogging algorithm is more natural than the results of the existing algorithm, and the disadvantage of high color saturation of the existing algorithm is eliminated, and 2) the existing defogging algorithm is too rough to estimate the atmospheric light, and the error of the estimation result is large, so that the image restoration result is often a problem of color distortion. In order to solve this problem, a method for estimating the atmospheric light based on the statistics of the light source is proposed, which mainly uses the prior knowledge of the distribution of the atmospheric light sample at the light source point. The method comprises the following steps of: firstly, selecting a part of the possible atmospheric light source points in the original drawing, carrying out the clustering on the part of the atmospheric light source point by a non-supervised clustering algorithm, clustering a plurality of alternative atmospheric light source spots, and counting the number of sample points contained in each cluster, the cluster of points with the largest number of points is selected to solve the atmospheric light. the luminance average vector of each atmospheric light sample point in the cluster is used as the estimated value of the atmospheric light, and the geometric center of each cluster is used as the position of the atmospheric light. The experimental results show that the light intensity vector and the position of the light source are more accurate, which makes the image restoration appear more natural in the subjective vision. in that prior method, a fixed numb of atmospheric light sample points are adopt for clustering, and the atmospheric light value is estimated at the most point cluster containing the candidate points. As the sample point is small, the estimated atmospheric light value is in a statistically significant error. In order to solve this problem, the method of threshold division is used to select the sample point of the atmospheric light, so as to improve the number of the sample points of the atmospheric light, and the clustering of the atmospheric light spot by the ant colony algorithm is used to improve the accuracy of the estimation result of the atmospheric light value. In order to improve the calculation efficiency of the algorithm, the paper first uses the K-means algorithm to carry out the preliminary clustering of the atmospheric light candidate points, and then uses the ant colony algorithm to improve the clustering result. the experimental results show that the atmospheric light value estimated by the algorithm can make the defogging result look more natural and can further improve the image quality evaluation index of the defogging result. In the actual scene, the distribution of the atmospheric light in each area is not uniform, and this prior knowledge is used, and an atmospheric light estimation algorithm based on the Gaussian distribution is proposed in this paper. the algorithm selects the candidate points in a way of threshold division to increase the number of initial sample points, and simultaneously introduces a clustering algorithm to combine the light source spot clusters obtained by the original algorithm so as to improve the number of sample points contained in a single dot cluster. Unreasonable point clusters are filtered out by using a proportional threshold, and each cluster is considered as a single light source to separately calculate the effect on the surrounding pixels, and the two-dimensional Gaussian distribution function is used for modeling the cluster, and finally, a position-related atmospheric light diagram is generated in place of the global atmospheric light. The experimental results show that the result of using the Gaussian distribution atmospheric photo-graph is more natural in the subjective vision than the original algorithm, and is also improved in the image quality evaluation index.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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相關(guān)期刊論文 前10條
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