本文選題：單幅圖像去霧 + 暗原色原理��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：人類獲取外界的信息量超過70%來自于視覺,隨著工業(yè)水平的快速發(fā)展以及生活水平的提升,霧霾天氣出現(xiàn)的頻率加快、程度加深。霧霾天氣使得戶外成像系統(tǒng)獲得的圖像視頻降質(zhì)。為了從霧霾天氣獲得的戶外圖像中提取出準(zhǔn)確、足夠的信息,需要對有霧圖像進(jìn)行去霧處理,使得能夠應(yīng)用到交通道路監(jiān)控、遙感監(jiān)測等方面。本文主要工作基于何愷明提出的暗原色原理進(jìn)行單幅圖像去霧。首先,深入研究何愷明提出的去霧算法:第一步,估計(jì)圖像的粗略透射率;第二步,優(yōu)化粗略透射率,本文利用何愷明提出的軟摳圖和引導(dǎo)濾波兩種方法實(shí)現(xiàn);第三步,利用大氣散射模型進(jìn)行參數(shù)反演獲得去霧圖像。然后,分析了何愷明的方法的缺點(diǎn)并提出改進(jìn)方案:(1)利用四分加權(quán)法進(jìn)行大氣光估計(jì),避免當(dāng)圖像中存在大片淺色非天空區(qū)域造成的誤差;(2)利用鄰域相似暗原色先驗(yàn)估計(jì)透射率,能夠解決軟摳圖造成的時(shí)間復(fù)雜度高問題和消除引導(dǎo)濾波過平滑導(dǎo)致的光暈現(xiàn)象。然后,利用大氣散射模型和本文算法估計(jì)的大氣光值和透射率得到去霧圖像。最后,設(shè)計(jì)GUI界面用于顯示輸入的有霧圖像和三種算法的去霧后圖像,以及對三種算法的客觀評價(jià)指標(biāo),包括運(yùn)行時(shí)間、峰值信噪比、平均結(jié)構(gòu)相似性、新增可見邊之比、可見邊的規(guī)范化梯度均值和飽和黑色或白色像素點(diǎn)的百分比。本文利用以上三種算法分別對自然圖像、含人物的圖像、含建筑物的圖像以及車牌圖像四類進(jìn)行去霧并對每一幅圖像進(jìn)行評價(jià),改進(jìn)后的算法具有如下幾個(gè)優(yōu)點(diǎn):(1)時(shí)間復(fù)雜度大大降低(一幅600*450的圖像,本文算法相比于使用軟摳圖的算法和使用引導(dǎo)濾波的算法分別降低了89.5%和35%);(2)能夠消除由引導(dǎo)濾波過平滑造成的光暈現(xiàn)象;(3)綜合主觀評價(jià)方法和客觀評價(jià)方法評估出本文的改進(jìn)算法的去霧能力,其能在保持何愷明算法效果的基礎(chǔ)上獲得更多的細(xì)節(jié)信息。同時(shí),本文算法存在需要改進(jìn)的地方:(1)去霧后的圖像可能出現(xiàn)顏色過飽和的現(xiàn)象,(2)透射率估計(jì)選用的窗口大小也會(huì)影響去霧結(jié)果。
[Abstract]:With the rapid development of industry and the improvement of living standard, the frequency and degree of haze weather appear faster and deeper with the rapid development of industry and the improvement of living standard. Haze weather allows outdoor imaging systems to obtain image video degradation. In order to extract accurate and sufficient information from outdoor images obtained from haze weather it is necessary to defog the foggy images so that they can be applied to traffic road monitoring remote sensing monitoring and so on. The main work of this paper is to defog a single image based on the principle of dark primary color proposed by Ho Kaiming. Firstly, the de-fogging algorithm proposed by Ho Kaiming is studied in depth: the first step is to estimate the rough transmittance of the image; the second step is to optimize the rough transmittance. In this paper, the soft matting method and the guided filtering method proposed by Ho Kai-ming are used to realize the algorithm. The model of atmospheric scattering is used for parameter inversion to obtain the defog image. Then, the shortcomings of Ho Kai-ming 's method are analyzed and an improved scheme is put forward: 1) the atmospheric light estimation is carried out by using the quadrilateral weighting method. To avoid the error caused by large light-colored non-sky region in the image we can solve the problem of high time complexity caused by soft matting and eliminate the halo caused by over-smoothing of guided filter by using neighborhood similar dark primary color priori estimation of transmittance. Then, using the atmospheric scattering model and the atmospheric light value and transmittance estimated by the present algorithm, the defogging image is obtained. Finally, the GUI interface is designed to display the inputted foggy image and the post-fogging image of the three algorithms, and the objective evaluation indexes of the three algorithms, including running time, peak signal-to-noise ratio (PSNR), average structural similarity, and the ratio of visible edges added. The normalized gradient mean of the visible edge and the percentage of saturated black or white pixels. In this paper, the above three algorithms are used to defog and evaluate the natural image, the image of the person, the image of the building and the image of license plate, respectively. The improved algorithm has the following advantages: 1) time complexity is greatly reduced (an image of 600,450, Compared with the soft matting algorithm and the guided filter algorithm, the proposed algorithm can reduce 89. 5% and 35%, respectively.) it can eliminate the halo phenomenon caused by the guiding filter smoothing.) the comprehensive subjective evaluation method and the objective evaluation method can be evaluated. In this paper, the improved algorithm of the ability to remove fog, It can obtain more details on the basis of maintaining the effect of Ho Kaiming algorithm. At the same time, there is a need for improvement in this algorithm. (1) the phenomenon of color supersaturation may occur in the image after fog removal. (2) the window size selected for the transmission estimation will also affect the de-fogging results.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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本文編號：1860051