發(fā)布時間：2018-06-07 03:36

  本文選題：圖像修復(fù) + 樣本��； 參考：《西北大學(xué)》2016年博士論文

【摘要】：圖像修復(fù)是圖像處理和模式識別領(lǐng)域中非常重要的分支,近年來已經(jīng)引起越來越多研究人員的關(guān)注。其基本思想是根據(jù)破損圖像中的有效信息,對破損區(qū)域中的缺損信息進行有效估計,使修復(fù)之后的圖像在整體上更加協(xié)調(diào),并且使不熟悉原始圖像的人覺察不到修復(fù)痕跡。目前,圖像修復(fù)技術(shù)在老照片和珍貴文獻(xiàn)資料的修復(fù)、文物保護、機器人視覺等各個領(lǐng)域發(fā)揮了越來越重要的作用。因此,對圖像修復(fù)方法進行廣泛深入研究具有非常重要的現(xiàn)實意義。本文首先對圖像修復(fù)的相關(guān)方法以及稀疏表示的相關(guān)理論進行研究。在此基礎(chǔ)上,針對現(xiàn)有方法中存在的修復(fù)順序不合理問題、錯誤匹配問題以及貪婪性問題,對大尺度破損的圖像修復(fù)展開深入研究。論文的主要工作和創(chuàng)新點如下：(1)提出一種基于結(jié)構(gòu)稀疏度的圖像修復(fù)方法。針對傳統(tǒng)的圖像修復(fù)方法中存在優(yōu)先權(quán)迅速降低導(dǎo)致修復(fù)順序不合理的問題,利用塊結(jié)構(gòu)稀疏度和鄰域像素差異度對優(yōu)先權(quán)進行定義,使修復(fù)順序更加合理。其次,針對修復(fù)過程中存在錯誤匹配以及錯誤累積的問題,利用塊間距離和塊內(nèi)距離對匹配規(guī)則進行定義,可以有效避免圖像塊的錯誤匹配,并避免錯誤不斷累積,進而提高修復(fù)效果。(2)提出一種基于樣本和稀疏表示的混合修復(fù)方法。針對傳統(tǒng)方法存在塊的錯誤匹配以及錯誤不斷累積的問題,將基于樣本的修復(fù)方法與基于稀疏表示的修復(fù)方法進行融合,利用各自方法的優(yōu)點,使它們互為補充。如果沒有發(fā)生錯誤匹配,則用基于樣本的方法進行修復(fù),可以保持紋理的多樣性和完整性;否則用基于稀疏表示的方法進行修復(fù),可以及時糾正匹配錯誤,防止錯誤不斷累積。該方法可以使修復(fù)圖像符合人類主觀視覺要求。(3)提出一種基于主成分分析(Principal Component Analysis, PCA)分類的快速圖像修復(fù)方法。針對傳統(tǒng)的圖像修復(fù)方法中需要全局遍歷搜索匹配樣本塊,導(dǎo)致比較耗時的問題,利用PCA方法將圖像塊分為平滑塊、邊緣塊和紋理塊三類。對于平滑塊,采用基于DCT字典的稀疏表示方法進行修復(fù),不需要全局遍歷搜索樣本塊；對于邊緣塊,將其搜索區(qū)域設(shè)置為其周圍鄰域,縮小搜索區(qū)域；對于紋理塊,為了保證紋理的多樣性,仍采取全局遍歷搜索。該方法可以有效減少匹配樣本塊的搜索時間,因而可以提高圖像的修復(fù)效率。(4)提出一種基于形態(tài)成分分析(Morphological Component Analysis, MCA)的邊緣提取方法。從圖像修復(fù)的角度出發(fā),邊緣提取的根本目的是提取對象的主要邊緣輪廓,并且盡可能避免由復(fù)雜紋理細(xì)節(jié)造成的孤立和瑣碎的邊緣。基于以上考慮,首先利用MCA方法把圖像進行分解,得到平滑層和紋理層,然后在平滑層上利用Otsu算法估計自適應(yīng)閾值,最后根據(jù)非極大值抑制算法對圖像的邊緣進行提取。該方法可以避免過多復(fù)雜紋理對邊緣圖像的影響,使邊緣圖像只保留對象的主要輪廓。(5)提出一種基于邊緣引導(dǎo)和非局部均值的修復(fù)方法。針對傳統(tǒng)的圖像修復(fù)方法不能很好地保持對象輪廓的連續(xù)性和完整性的問題,首先利用基于MCA的邊緣提取方法提取邊緣圖像,并對破損的邊緣進行修復(fù)。并針對非局部均值的修復(fù)方法容易導(dǎo)致紋理細(xì)節(jié)模糊的問題,提出一種基于非局部均值的自適應(yīng)方法。然后,在已修復(fù)邊緣的引導(dǎo)下,利用非局部均值的自適應(yīng)修復(fù)方法分別對破損圖像的邊緣區(qū)域和其余區(qū)域進行修復(fù)。該方法可以有效保護對象輪廓的連續(xù)性,提高圖像修復(fù)效果。
[Abstract]:Image restoration is a very important branch in the field of image processing and pattern recognition. In recent years, more and more researchers have attracted more and more attention. The basic idea is to effectively estimate the defect information in damaged area according to the effective information in damaged image, so that the image after repair is more coordinated and unfamiliar. At present, image restoration technology has played a more and more important role in the restoration of old photos and precious literature, cultural relics protection, robot vision and so on. Therefore, it is of great practical significance to carry out extensive and in-depth research on image restoration methods. In this paper, the main work and innovation of this paper are as follows: (1) the main work and innovation of this paper are as follows: (1) a kind of knot based on the knot is put forward. In view of the problem that the priority of the traditional image restoration is rapidly reduced, the priority is defined by the block sparsity and the neighborhood pixel difference degree, which makes the repair order more reasonable. Secondly, there are error matching and error accumulation in the repair process. With the use of inter block distance and intra block distance to define the matching rules, it can effectively avoid error matching of image blocks, avoid continuous accumulation of errors and improve the effect of repair. (2) a hybrid restoration method based on sample and sparse representation is proposed. The problem is to combine the repair method based on the sample and the repair method based on the sparse representation, and make use of the advantages of each method to complement each other. If there is no error matching, the sample based method is used to repair it, and the diversity and integrity of the texture can be maintained; otherwise, the method based on sparse representation can be used to repair it. In time, matching errors can be corrected in time to prevent errors from accumulating. This method can make the restored image conform to the human subjective vision requirements. (3) a fast image restoration method based on the Principal Component Analysis (PCA) classification is proposed. Block, resulting in more time-consuming problems, the image block is divided into three categories: smooth block, edge block and texture block by using the PCA method. For smooth blocks, the sparse representation method based on DCT dictionary is used to repair, without global traversal search sample blocks; for edge blocks, the search area is set to its neighborhood, and the search area is narrowed. Texture block, in order to ensure the variety of texture, still take global traversal search. This method can effectively reduce the search time of the matching sample block, and thus can improve the efficiency of image restoration. (4) a method of edge extraction based on Morphological Component Analysis (MCA) is proposed. The fundamental purpose of edge extraction is to extract the main edge contour of the object, and avoid the isolated and trivial edges caused by the details of the complex texture as much as possible. Based on the above consideration, the image is decomposed by the MCA method, and the smooth layer and texture layer are obtained. Then the adaptive threshold is estimated by Otsu algorithm on the smooth layer, and the final root is obtained. The edge of the image is extracted according to the non maximum value suppression algorithm. This method can avoid the influence of too many complex textures on the edge image, so that the edge image can only retain the main contour of the object. (5) a restoration method based on edge guidance and non local mean is proposed. The problem of the continuity and integrity of the profile is to extract the edge images by using the edge extraction method based on MCA and repair the damaged edges. A self-adaptive method based on the non local mean is proposed for the problem that the non local mean restoration method can easily lead to the blurred texture details. At the same time, the adaptive repair method of non local mean is used to repair the edge region and the rest of the damaged image respectively. This method can effectively protect the continuity of the object contour and improve the image restoration effect.
【學(xué)位授予單位】：西北大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 胡海平;劉曉振;;一種基于非局部BSCB模型的圖像修復(fù)方法[J];應(yīng)用數(shù)學(xué)與計算數(shù)學(xué)學(xué)報;2015年03期

2 王猛;翟東海;聶洪玉;王佳君;;鄰域窗口權(quán)重變分的圖像修復(fù)[J];中國圖象圖形學(xué)報;2015年08期

3 張東;唐向宏;張少鵬;黃俊澤;;小波變換與紋理合成相結(jié)合的圖像修復(fù)[J];中國圖象圖形學(xué)報;2015年07期

4 康佳倫;唐向宏;張東;屠雅麗;;特征分類學(xué)習(xí)的結(jié)構(gòu)稀疏傳播圖像修復(fù)方法[J];計算機輔助設(shè)計與圖形學(xué)學(xué)報;2015年05期

5 張晴;林家駿;劉云翔;;基于局部平均灰度熵的快速圖像修復(fù)算法[J];計算機應(yīng)用與軟件;2014年10期

6 胡文瑾;李戰(zhàn)明;劉仲民;;快速非局部均值形態(tài)成分分析唐卡圖像修復(fù)算法[J];計算機輔助設(shè)計與圖形學(xué)學(xué)報;2014年07期

7 翟東海;段維夏;魚江;;基于雙十字TV模型的圖像修復(fù)算法(英文)[J];電子科技大學(xué)學(xué)報;2014年03期

8 張申華;王克剛;祝軒;;局部特征信息約束的改進Criminisi算法[J];計算機工程與應(yīng)用;2014年08期

9 尹雯;李元祥;周則明;劉世前;;基于稀疏表示的遙感圖像融合方法[J];光學(xué)學(xué)報;2013年04期

10 李志丹;和紅杰;尹忠科;陳帆;仁青諾布;;基于塊結(jié)構(gòu)稀疏度的自適應(yīng)圖像修復(fù)算法[J];電子學(xué)報;2013年03期

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本文編號：1989579