本文關(guān)鍵詞： 多幀影像 航空成像 超分辨 Papoulis-Gerchberg 自學(xué)習(xí) 字典 局部線性嵌入　出處：《中國科學(xué)院長春光學(xué)精密機(jī)械與物理研究所》2017年博士論文　論文類型：學(xué)位論文

【摘要】：隨著航空光電載荷的高速發(fā)展,更大的畫幅、更高的圖像分辨率以及更遠(yuǎn)的作用距離成為航空光電載荷不斷追求的目標(biāo),但受體積、重量、功耗以及光學(xué)系統(tǒng)成像過程中引起的欠采樣、運(yùn)動(dòng)模糊及噪聲等因素影響,航空圖像分辨率不能滿足實(shí)際應(yīng)用的需求,因此獲取高分辨率(HR)航拍圖像已成為當(dāng)今航空領(lǐng)域的熱點(diǎn)和難點(diǎn)。提高圖像分辨率最直接的方法是采用高分辨率CCD相機(jī),但受工藝水平以及相機(jī)圖像數(shù)據(jù)傳輸速率的限制,通過高分辨率CCD相機(jī)采樣得到的圖像分辨率的能力是十分有限的。近年來,通過信號處理方式提升圖像分辨率,即超分辨技術(shù)受到廣泛關(guān)注。超分辨技術(shù)即是在不改變原有硬件成像系統(tǒng)基礎(chǔ)上,僅通過軟件方法,也就是利用信號處理的方法將一幅或多幅包含相似信息而細(xì)節(jié)不同的低分辨率(LR)圖像重構(gòu)成一幅高分辨率圖像。"超"即是克服傳統(tǒng)低分辨成像系統(tǒng)固有衍射極限,獲取超出光學(xué)系統(tǒng)衍射極限以外的空間頻率信息,實(shí)現(xiàn)進(jìn)一步提高分辨率的工程應(yīng)用目的。本文首先介紹超分辨成像技術(shù)的研究背景和工程應(yīng)用,系統(tǒng)的總結(jié)、分析和比較了超分辨成像技術(shù)的物理成像模型、方法類別和評價(jià)體系。在建立的成像模型基礎(chǔ)上,針對現(xiàn)有超分辨算法運(yùn)算復(fù)雜度,邊緣模糊,圖像失真等問題,以多幀航空影像為研究對象,圍繞多幀圖像的超分辨成像技術(shù)主題展開了深入研究。主要研究工作如下:1.為進(jìn)一步提高拍攝圖像的分辨率,提出一種改進(jìn)的Papoulis-Gerchberg超分辨算法,新算法提出邊緣檢測方法,可以改善傳統(tǒng)方法空間復(fù)雜度和重構(gòu)圖像邊緣模糊的問題,新算法在原有的算法基礎(chǔ)上融于邊緣檢測,針對多幅同一場景輸入圖像,在每次Papoulis-Gerchberg迭代過程加入坎尼檢測,同時(shí)將每步的重構(gòu)誤差投影到下一步重構(gòu)過程,降低了算法空間復(fù)雜度,能有效恢復(fù)丟失的邊緣高頻信息。與現(xiàn)有的經(jīng)典超分辨重構(gòu)方法相比,本算法反映圖像質(zhì)量的峰值信噪比和灰度標(biāo)準(zhǔn)差更高,去除了原始重構(gòu)方法圖像邊緣疊影現(xiàn)象,有效提高了原始輸入圖像的分辨率。2.研究了軟硬件相結(jié)合的超分辨成像技術(shù),首先用探測器掃描獲得同一場景的彼此錯(cuò)位亞象元像素的多幀圖像作為訓(xùn)練樣本和輸入圖像,然后針對傳統(tǒng)局部線性嵌入(LLE)實(shí)例學(xué)習(xí)超分辨算法過于依賴外部訓(xùn)練樣本,不利于光電成像系統(tǒng)直接處理等缺點(diǎn),提出一種基于自學(xué)習(xí)的改進(jìn)局部線性嵌入(LLE)算法,采用新的LLE權(quán)值計(jì)算方法以獲得正數(shù)權(quán)值,同時(shí)對初始估計(jì)再次運(yùn)用自學(xué)習(xí)LLE方法恢復(fù)丟失的高頻細(xì)節(jié)信息,最終能獲得高質(zhì)量的重構(gòu)圖像,能滿足高質(zhì)量高分辨率的成像需求。3.針對基于字典學(xué)習(xí)超分辨重構(gòu)方法需要大量的HR-LR圖像訓(xùn)練冗余字典,且若選取的HR-LR訓(xùn)練圖像不含有待重構(gòu)低分辨圖像的頻率信息,重構(gòu)出的高分辨圖像會(huì)造成失真等缺點(diǎn),提出自學(xué)習(xí)字典的多幅超分辨率重構(gòu)方法,用待重構(gòu)的多幅同一場景不同運(yùn)動(dòng)參數(shù)的低分辨率圖像做為輸入圖像和訓(xùn)練圖像,分塊學(xué)習(xí)字典,重構(gòu)出高一尺度圖像,并加入到訓(xùn)練圖像中,如此依次逐級構(gòu)造不同尺度圖像做為訓(xùn)練圖像集,最終重構(gòu)出達(dá)到或最接近目標(biāo)圖像尺度大小的多幅高分辨率圖像。最后利用NLM思想將得到的多幅高分辨率圖像融合成一幅目標(biāo)圖像尺度大小的最終重構(gòu)高分辨率圖像。仿真實(shí)驗(yàn)結(jié)果表明,本文算法的重構(gòu)圖像信噪比更高,細(xì)節(jié)細(xì)膩,能從拍攝圖像中獲得更清晰的高分辨圖像。4.提出一種基于自適應(yīng)的高性能超分辨算法,通過將基于學(xué)習(xí)與基于重構(gòu)的超分辨算法相結(jié)合,充分利用兩者的優(yōu)點(diǎn),本文不需外部訓(xùn)練圖像,首先以輸入圖像做為訓(xùn)練圖像創(chuàng)建字典塊集,其次在訓(xùn)練獲得的訓(xùn)練塊集中利用自適應(yīng)學(xué)習(xí)方法獲取HR圖像塊中心點(diǎn)像素值,然后利用高頻恢復(fù)方法重構(gòu)丟失的高頻邊緣信息,最后結(jié)合基于重構(gòu)方法,提出用邊緣做為先驗(yàn)知識滿足重構(gòu)約束,獲取最終的高分辨重構(gòu)圖像。本文算法同時(shí)解決了基于重構(gòu)算法邊緣模糊和基于學(xué)習(xí)算法失真的缺點(diǎn),獲得了高質(zhì)量的高分辨率圖像,對于提升航空圖像分辨率具有很重要的意義。本文針對目前各種超分辨算法的失真模糊等一系列問題,圍繞多幀影像超分辨成像技術(shù)進(jìn)行了探索,取得了階段性成果,這些成果為進(jìn)一步的工程實(shí)踐和成熟應(yīng)用提供了理論基礎(chǔ),對航空圖像超分辨成像具有一定指導(dǎo)意義。
[Abstract]:With the rapid development of aviation photoelectric payload, the bigger picture, the image resolution and further distance more become the constant pursuit of the goal of aviation photoelectric payload, but by the volume, weight, power consumption and undersampling caused by the optical system imaging process, motion blur and noise and other factors, can not meet the actual resolution of aerial image the needs of the application, thus obtaining high resolution aerial images (HR) has become a hot and difficult point in the field of aviation. The method of improving the image resolution is the most direct use of high resolution CCD camera, but by the technology level and the limitation of camera image data transmission rate, the ability of image resolution by high resolution CCD camera is sampled very limited. In recent years, to improve the image resolution by signal processing methods, namely super resolution technology has attracted extensive attention. Super resolution technology is not in Change the original hardware based imaging system, only through the software method, which is using the signal processing method to one or more images containing similar information and details of different low resolution (LR) image of a high resolution image. The "super" is to overcome the traditional low resolution imaging system to obtain the inherent diffraction limit. The spatial frequency information outside the optical system beyond the diffraction limit, can further improve the resolution of engineering application. This paper first introduces the research background and application of super resolution imaging technology, system summary, analysis and comparison of the physical imaging super resolution imaging model, method of category and evaluation system. Based on imaging model based on the in view of the existing super-resolution algorithm, computational complexity, fuzzy edge, image distortion and other issues, through the multi frame image as the research object, the super-resolution imaging on multi frame images The theme of technology is deeply studied. The main research work is as follows: 1. in order to further improve the image resolution, this paper proposes an improved Papoulis-Gerchberg super-resolution algorithm, the new algorithm of edge detection method, can improve the traditional method of space complexity and reconstruction of image edge fuzzy problem, a new algorithm based on the original algorithm on the melt to the edge detection for multiple input images in the same scene, each iteration of the Papoulis-Gerchberg process to join the canny, while the projection reconstruction error of each step into the reconstruction process the next step, reduces the space complexity of the algorithm, can effectively restore the high-frequency edge information loss. Compared with the existing classical super-resolution reconstruction method, this algorithm reflects the the image quality of the peak signal-to-noise ratio and standard deviation higher, removal of the original reconstruction method of image edge aliasing phenomenon, effectively improves the original input Super resolution imaging technology for image resolution.2. of combination of hardware and software, first obtain the same scene stagger subpixel pixel multi frame images as training samples and input images with the detector scan, then the traditional local linear embedding (LLE) case study super-resolution algorithm is too dependent on external training samples, disadvantages to direct processing of photoelectric imaging system, we put forward an improved self-learning based on local linear embedding (LLE) algorithm, using LLE weighted new calculation method to obtain positive weights, and once again the use of high frequency information learning LLE method to recover the lost on the initial estimation, finally to obtain high quality images, can meet the high quality high resolution imaging.3. super-resolution reconstruction method for dictionary learning based on the need of a large number of HR-LR image training redundant dictionary, and if the selected HR-LR training Do not contain low resolution image to reconstruct the frequency information of the image, the high resolution image will cause the distortion of reconstructed defects, put forward multi frame super-resolution reconstruction method for learning the dictionary, using low resolution images to reconstruct multiple images of the same scene with different motion parameters as the input image and the training images, block learning the dictionary, to reconstruct the high resolution images, and added to the training images, so the images of different scale structure followed step by step as the training image set is reconstructed more images of higher resolution close to the target image size. The fusion of multiple images of higher resolution got into the final reconstruction of high-resolution image the size of the target images with NLM. Simulation results show that this algorithm reconstruction SNR is higher, the details, from the captured image more clear and high .4. presents a high resolution image super-resolution algorithm based on adaptive performance, the learning based super-resolution algorithm and reconstruction based on the combination, make full use of their advantages, this paper does not need external training image, first of all to the input image as the training images to create a dictionary block set, then in training to obtain the training block by adaptive learning method to get the center of the block HR image pixel values, then the high-frequency edge information recovery method using high frequency loss reconstruction, finally based on the reconstruction method is proposed for edge reconstruction constraints do meet as prior knowledge, to acquire high resolution image reconstruction in the end. This algorithm solves the reconstruction algorithm and learning algorithm of fuzzy edge distortion defects based on high resolution, high quality images were obtained, has a very important significance for improving image resolution. Aiming at the air All kinds of super resolution algorithm of fuzzy distortion of a series of problems such as multi frame image super-resolution imaging on technology are explored, and achieved initial results, these results provide a theoretical basis for further engineering practice and mature application of aerial image super-resolution imaging has certain guiding significance.

【學(xué)位授予單位】：中國科學(xué)院長春光學(xué)精密機(jī)械與物理研究所
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 譚璐,朱矩波,吳翊;基于最優(yōu)一維分解的圖像超分辨方法[J];中國圖象圖形學(xué)報(bào);2004年04期

2 宋艷玲;;圖像超分辨重建技術(shù)研究現(xiàn)狀[J];科技創(chuàng)新導(dǎo)報(bào);2012年09期

3 William F.Gabriel ,洪大光 ,李吉良;譜分析與自適應(yīng)陣列的超分辨技術(shù)[J];系統(tǒng)工程與電子技術(shù);1981年12期

4 劉志文,柯有安;一種基于模型的雷達(dá)超分辨方法[J];系統(tǒng)工程與電子技術(shù);1993年12期

5 魏勁松,阮昊,施宏仁,干福熹;一種新的超分辨記錄點(diǎn)的讀出技術(shù)[J];光學(xué)學(xué)報(bào);2003年05期

6 孟婕;丁志華;周琳;;光學(xué)相干層析成像軸向超分辨研究[J];光子學(xué)報(bào);2008年03期

7 劉妍妍;張新;張建萍;;超分辨重建技術(shù)及其研究進(jìn)展[J];中國光學(xué)與應(yīng)用光學(xué);2009年02期

8 韓軍;薛小樂;王星;;光電成像系統(tǒng)超分辨成像技術(shù)方法研究[J];應(yīng)用光學(xué);2011年01期

9 王繼良,段鳳增;最大熵法在高頻雷達(dá)角度超分辨中的應(yīng)用[J];系統(tǒng)工程與電子技術(shù);1993年10期

10 張鋒,王陽,徐文東,顧冬紅,干福熹;超分辨近場結(jié)構(gòu)技術(shù)及其應(yīng)用[J];光學(xué)技術(shù);2003年05期

相關(guān)會(huì)議論文 前10條

1 喬延利;葉松;方黎;洪津;方勇華;荀毓龍;;超分辨空間外差光譜成像技術(shù)[A];第六屆成像光譜技術(shù)與應(yīng)用研討會(huì)文集[C];2006年

2 于建強(qiáng);袁景和;方曉紅;;超分辨多功能熒光顯微成像[A];中國化學(xué)會(huì)第29屆學(xué)術(shù)年會(huì)摘要集——第03分會(huì)：分析可視化及交叉學(xué)科新方法[C];2014年

3 李大勇;吳樂南;;特定條件下的圖像超分辨重建快速算法[A];第十三屆全國圖象圖形學(xué)學(xué)術(shù)會(huì)議論文集[C];2006年

4 程曉東;曹軒;何彥;;超分辨等離激元散射光成像研究[A];第十七屆全國光散射學(xué)術(shù)會(huì)議摘要文集[C];2013年

5 朱明強(qiáng);張國峰;李沖;龔文亮;嚴(yán)慧;;熒光分子開關(guān)用于超分辨光學(xué)成像[A];中國化學(xué)會(huì)第29屆學(xué)術(shù)年會(huì)摘要集——第21分會(huì)：光化學(xué)[C];2014年

6 鄒華;朱衛(wèi)華;嵇陽;吳建偉;;基于超分辨對衍射光斑的壓縮整形[A];魯豫贛黑蘇五省光學(xué)（激光）學(xué)會(huì)2011學(xué)術(shù)年會(huì)論文摘要集[C];2011年

7 陳薇;陳丹妮;齊t，

本文編號：1500112