本文選題：半影纖維　切入點(diǎn)：形態(tài)學(xué)重構(gòu)　出處：《昆明理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：數(shù)字圖像處理(Digital Image Processing)是將圖片信息轉(zhuǎn)換成數(shù)字信息然后利用計(jì)算機(jī)對(duì)其進(jìn)行處理的過(guò)程。主要是利用計(jì)算機(jī)對(duì)數(shù)字圖像進(jìn)行噪聲去除、圖像復(fù)原、圖像分割以及特征提取等,用來(lái)改善原始圖像的質(zhì)量,使得原始圖像更加清晰,從而得以保存和研究的目的。目前,隨著技術(shù)的不斷發(fā)展,數(shù)字圖像處理也向著更高,更深層方向發(fā)展。為天文學(xué)領(lǐng)域的發(fā)展提供了重要的技術(shù)基礎(chǔ)。太陽(yáng)每時(shí)每刻都發(fā)生著劇烈的變化,在光球?qū)又泻谧拥倪\(yùn)動(dòng)是太陽(yáng)活動(dòng)中典型的活動(dòng)之一。而黑子是由本影與半影構(gòu)成,其中半影中包含了亮纖維和暗纖維。研究表明,黑子中有巨大的磁場(chǎng)存在,同樣半影上方也有磁場(chǎng)。并且半影之所以呈現(xiàn)纖維狀也是與磁場(chǎng)的存在有關(guān)。同時(shí),研究發(fā)現(xiàn)半影纖維與埃Wr謝德流的形成以及熱等離子體的流動(dòng)有密切的聯(lián)系。所以為了更好的研究半影纖維形成與磁場(chǎng)的關(guān)系,以及埃Wr謝德流的形成等首先需要識(shí)別并研究半影纖維的特性。因此,識(shí)別半影纖維以及研究其相關(guān)特性是非常重要的。通過(guò)觀察發(fā)現(xiàn),亮纖維是呈彗星狀,即靠近本影方向有個(gè)局部較亮的點(diǎn),后面呈絲狀。所以,根據(jù)亮纖維的形態(tài)特征我們通過(guò)識(shí)別亮纖維中局部較亮的點(diǎn),并且分析其相關(guān)特性來(lái)代表半影亮纖維的相關(guān)特性。目前,大多數(shù)識(shí)別半影纖維亮點(diǎn)的方法是基于空域平滑的方法,其本質(zhì)是通過(guò)選用合適的閾值來(lái)提取半影纖維亮點(diǎn),但是閾值的選取是比較繁雜的過(guò)程,需要人為的不斷調(diào)試。同時(shí),其結(jié)果的好壞也是由人眼觀測(cè)來(lái)判斷,因此存在一定的主觀性。所以,為了解決以上問(wèn)題,本文提出了新的識(shí)別方法,運(yùn)用形態(tài)學(xué)重構(gòu)技術(shù)識(shí)別半影纖維亮點(diǎn)。具體識(shí)別過(guò)程如下:首先,使用線性濾波技術(shù)處理圖片降低圖像的噪聲;然后,提取半影區(qū)域。提取半影區(qū)域首先需要提取本影區(qū)域和黑子與米粒邊界,然后才能獲得半影區(qū)域;最后,在半影區(qū)域中通過(guò)形態(tài)學(xué)重構(gòu)技術(shù)重構(gòu)圖片,并得到原圖與重構(gòu)圖的差值圖,并經(jīng)過(guò)圖像歸一化,二值化等形態(tài)學(xué)操作提取半影纖維亮點(diǎn)。本文為了更好的展示識(shí)別過(guò)程,選用了美國(guó)大熊湖太陽(yáng)天文臺(tái)1.6米太陽(yáng)望遠(yuǎn)鏡(New Solar Telescope,簡(jiǎn)稱NST)序列圖中第一幀圖像作為代表進(jìn)行了展示。同時(shí),為了驗(yàn)證所提方法的可行性和普適性,本文作了兩組對(duì)比試驗(yàn),第一組是通過(guò)運(yùn)用本文所提方法來(lái)識(shí)別云南天文臺(tái)撫仙湖真空望遠(yuǎn)鏡(New Vaccum Sloar Telescope,簡(jiǎn)稱 NVST)和太陽(yáng)能光學(xué)望遠(yuǎn)鏡(Solar Optical Telescope,簡(jiǎn)稱 SOT)的數(shù)據(jù),從整體上驗(yàn)證方法的普適性。第二組是通過(guò)模擬已有方法來(lái)識(shí)別得到結(jié)果圖,并與本文識(shí)別結(jié)果圖作對(duì)比進(jìn)而證明方法的可行性。并且,為了評(píng)價(jià)本文所提算法,我們通過(guò)主觀評(píng)價(jià)和客觀評(píng)價(jià)兩方面展開(kāi)了闡述。首先主觀評(píng)價(jià),本文從三種數(shù)據(jù)(分別是:SOT,NVST,NST)中隨機(jī)抽選5幀圖像,并用本文所提算法得到識(shí)別結(jié)果圖。采用人工標(biāo)記的方法分別統(tǒng)計(jì)了應(yīng)該有的半影纖維亮點(diǎn)的個(gè)數(shù),實(shí)際統(tǒng)計(jì)出的個(gè)數(shù)以及識(shí)別出的亮點(diǎn)中正確的個(gè)數(shù)和誤識(shí)別的個(gè)數(shù)。進(jìn)而得到正確率和誤識(shí)別率,從這兩方面進(jìn)行主觀評(píng)價(jià)�？陀^評(píng)價(jià),為了和已有文獻(xiàn)中的統(tǒng)計(jì)結(jié)果作對(duì)比,本文運(yùn)用所提算法識(shí)別SOT的序列圖(共計(jì)764張圖像),然后統(tǒng)計(jì)了識(shí)別出的半影纖維亮點(diǎn)的面積大小以及強(qiáng)度大小的相關(guān)特性,并與已有文獻(xiàn)中的結(jié)果作對(duì)比從客觀方面說(shuō)明方法的正確性。
[Abstract]:Digital image processing (Digital Image Processing) is the process of image information into digital information and then processed by computer. The main computer is used for noise removal and image restoration of digital image, image segmentation and feature extraction, used to improve the quality of the original image, the original image is more clear, in order to save and the purpose of the study. At present, with the continuous development of technology, digital image processing is more and more deep direction. Provides an important technical basis for the development of the field of astronomy. Every time the sun is undergoing dramatic changes, sunspots in the photosphere in motion is one of the typical activities and sunspots in solar activities. It is composed of umbra and penumbra, which contains a light fiber and penumbra dark fiber. The results show that there exist huge magnetic field in the same spot, on the penumbra We also have the field. And the penumbra showing fibrous and magnetic field exist. At the same time, the study found that formation is associated with Ethiopia Wr schede penumbra fiber flow and thermal plasma flow. So in order to study the fiber formation and better penumbral magnetic field, and the formation of Wr. At the first need to flow the identification and study of the penumbral filaments characteristics. Therefore, the identification and characterization of penumbra fiber is very important. Through the observation that the light fiber is a comet, which is near to the direction of a local umbra lighter, behind the filamentous. So, according to the morphological characteristics of light fiber we identified by local light fiber a bright point, related characteristics and analyze the relevant characteristics to represent the penumbra bright fiber. At present, most methods for discovering penumbral filaments highlight is based on the spatial smoothing method. Nature is by selecting appropriate threshold to extract the penumbra fiber highlights, but the threshold is more complicated, the need of human constantly debugging. At the same time, the result is judged by the human eye observation, so there is certain subjectivity. Therefore, in order to solve the above problems, this paper puts forward a new recognition method, using the morphological identification of reconstruction of penumbral filaments highlights. The specific identification process is as follows: firstly, using the linear filtering image reduce the image noise; then, extracting the penumbra region. The extraction of penumbra area necessary to extract the umbra region and the sunspot and grain boundary, and then to get the penumbra region; finally, through the reconstruction of morphological reconstruction technology of pictures in the penumbra region, and get the difference maps and reconstruction image, and through image normalization, binarization and morphological operations to extract the semi Ying fiber highlights. In order to better show the recognition process, the 1.6 meter solar telescope in Big Bear Lake Solar Observatory (New Solar Telescope, referred to as NST) the first frame of the image sequence diagram as a representative of the show. At the same time, in order to verify the applicability and feasibility of the proposed method in this paper p, two groups of contrast experiments, the first group is through the use of the identification of Fuxian Lake vacuum telescope of Yunnan Observatory, the proposed method (New Vaccum Sloar Telescope, referred to as NVST) and solar telescope (Solar Optical Telescope, referred to as SOT) data from the whole verification method is universal. The second group is simulated by existing methods to get recognition results, and the feasibility and the identification results are compared and proved diagram method. And, in order to evaluate the proposed algorithm, we through the two aspects of subjective evaluation and objective evaluation. Firstly, elaborates the main 瑙傝瘎浠，

本文編號(hào)：1569285