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  本文選題：并行計算 + 圖像修復��； 參考：《昆明理工大學》2016年碩士論文

【摘要】：Ha全日面太陽圖像觀測是目前監(jiān)測太陽活動的主要手段。全球擁有眾多太陽Hα像的觀測站點,雖然各站點在選址時篩選嚴格,但是實際觀測中仍會出現(xiàn)大量的有云天氣。高空云層導致所觀測的Ha全日面像上覆蓋有一層云污染,使得圖像上的太陽活動細節(jié)變得模糊不清。對云污染圖像的實時檢測和修復,將非常有利于觀測的進行和對實時觀測質量的判斷。為此,本文主要進行了如下幾個方面的研究：(一)在前人Ha全日面太陽圖像質量評價和畸變Ha全日面太陽圖像修復方法的基礎上,進一步研究了適合實時改正系統(tǒng)云污染檢測和修復的算法。(二)利用GPU,在CUDA環(huán)境下實現(xiàn)了Ha全日面云污染檢測的相關并行處理算法。包括：在判斷重度云污染圖像時,采用均值法并行求得太陽區(qū)域的中心和半徑,然后并行求得二值區(qū)域長短軸比值；在判斷圖像上是否有可修復云污染時,并行平移實現(xiàn)太陽區(qū)域中心化、并行實現(xiàn)雙線性插值法把圖像從直角坐標系轉換到極坐標系、并行雙調排序法對極坐標系下圖像等分四部分區(qū)域每行分別排序、并行計算四部分區(qū)域中值臨邊昏暗曲線的相關系數(shù)。(三)對于修復中的關鍵性步驟：濾波,本文深入研究了中值濾波、形態(tài)學濾波及頻域巴特沃斯低通濾波在CUDA中的并行實現(xiàn)方法。云污染修復即通過和一個標準全日面像模板對比,并通過濾波得到云層透過率,最后用云污染圖像除以這個云層透過率。使用SSIM算法對三種濾波方法的修復結果做質量評價后發(fā)現(xiàn),三者都能有效的濾除太陽上的活動細節(jié)。鑒于二階巴特沃斯低通濾波在處理速度上的突出優(yōu)勢,系統(tǒng)將其作為最優(yōu)選擇。(四)在微軟MFC框架下,通過調用在GPU中并行執(zhí)行的CUDA函數(shù)成功開發(fā)出一套完整的、具有可視化界面的Ha全日面圖像云污染實時檢測和修復的軟件系統(tǒng)。經(jīng)測試,本系統(tǒng)可以有效的區(qū)分出重度云污染、可修復云污染和無云污染的圖像,并能對云污染的程度進行量化。對于可修復的云污染,可以進行有效的實時修復。在我們的實驗環(huán)境下,1分鐘的觀測圖像,可以在0.7秒內完成一幅云污染圖像的處理,完全符合實時要求。
[Abstract]:Ha solar image observation is the main method to monitor solar activity at present. There are many observational stations of solar H 偽 image all over the world. Although the site selection is strict, there will still be a large number of cloud weather in the actual observation. High-altitude clouds cause a layer of cloud pollution over the observed Ha-Sun image, blurring the details of solar activity on the image. The real-time detection and restoration of cloud pollution images will be very helpful to the observation and the judgment of the real-time observation quality. Therefore, in this paper, we mainly study the following aspects: (1) on the basis of the quality evaluation of Ha solar image and the distorted Ha solar image restoration method, Furthermore, the algorithms suitable for cloud pollution detection and repair in real time correction system are studied. (2) using GPU to realize the parallel processing algorithm of Ha sun-surface cloud pollution detection in CUDA environment. It includes: when judging the image of heavy cloud pollution, the center and radius of the solar region can be obtained by means of the mean method, and the ratio of the long and short axis of the binary region can be obtained in parallel; when judging whether there is any remediable cloud pollution on the image, The parallel translation realizes the center of the solar region, the bilinear interpolation method transforms the image from the right-angle coordinate system to the polar coordinate system, and the parallel bi-tone sorting method sorts the image in four parts of the polar coordinate system. The correlation coefficients of the four region median edge darkening curves are calculated in parallel. (3) for the key steps of restoration: filtering, the parallel implementation methods of median filter, morphological filter and Butterworth low-pass filter in frequency domain in CUDA are studied in this paper. Cloud pollution remediation is achieved by comparing it with a standard full-day image template and filtering to obtain cloud transmittance. Finally the cloud contamination image is divided by this cloud transmittance. The SSIM algorithm is used to evaluate the quality of the restoration results of the three filtering methods. It is found that all of them can filter the moving details of the sun effectively. In view of the outstanding advantage of second-order Butterworth low-pass filter in processing speed, the system regards it as the best choice. (4) under the framework of Microsoft MFC, by calling the CUDA function executed in parallel in GPU, a complete real-time detection and repair system for cloud contamination of Ha sun-plane images with visual interface has been developed. After testing, the system can effectively distinguish the severe cloud pollution, can repair the cloud pollution and no cloud pollution images, and can quantify the degree of cloud pollution. For repairable cloud pollution, it can be effectively repaired in real time. In our experimental environment, one minute observation image can complete a cloud pollution image processing in 0.7 seconds, which fully meets the real-time requirements.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：P182.2;TP391.41

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本文編號：1945344