本文選題：局部模糊圖像 + 模糊特征��； 參考：《武漢大學(xué)》2017年碩士論文

【摘要】：圖像在信息的表達(dá)與傳遞中有著關(guān)鍵的作用,人們最常用來(lái)記錄圖像信息的工具就是相機(jī)。當(dāng)相機(jī)拍攝圖像時(shí)不可避免會(huì)存在圖像失真的情況,比如模糊、色偏等。而圖像退化后會(huì)大大地降低圖像信息表達(dá)的精準(zhǔn)度。本文首先分析了運(yùn)動(dòng)模糊與失焦模糊兩種局部模糊的形成機(jī)制。然后對(duì)比兩種局部模糊圖像的退化機(jī)理,尋找兩者的特點(diǎn)與區(qū)別。從研究運(yùn)動(dòng)模糊與失焦模糊圖像的形成機(jī)理中,也可同時(shí)發(fā)現(xiàn)模糊與清晰圖像間的差別。由于局部模糊圖像的模糊核不易精準(zhǔn)地計(jì)算,所以本文未采用估計(jì)圖像模糊核的方法來(lái)區(qū)分模糊的類型。而是提出了四項(xiàng)模糊特征用于區(qū)分模糊與清晰,并判斷圖像的模糊類型。提取多個(gè)模糊特征作為分類的特征數(shù)據(jù),是為了更好地從各方面表示圖像的信息。在圖像庫(kù)中,對(duì)所有的圖像塊采用SVM進(jìn)行學(xué)習(xí)與訓(xùn)練,建立圖像類型的預(yù)測(cè)模型。利用兩次SVM分類器可將圖像分類為清晰圖像、運(yùn)動(dòng)模糊圖像與失焦模糊圖像三類。最后,利用上述建立的SVM分類模型,對(duì)局部模糊圖像以每個(gè)像素為中心分塊進(jìn)行檢測(cè),判斷其是否為模糊,完成圖像局部模糊區(qū)域的檢測(cè)。由于圖像檢測(cè)存在誤判現(xiàn)象,所以最終檢測(cè)得到的二值化效果圖中會(huì)出現(xiàn)小空洞或不連接等現(xiàn)象。最后,采用數(shù)學(xué)形態(tài)學(xué)算法對(duì)最終的試驗(yàn)效果進(jìn)行完善。圖像局部模糊分類試驗(yàn)結(jié)果證明了本文提出的模糊特征能夠較為準(zhǔn)確地判斷圖像塊是否為模糊圖像,并且能夠辨別圖像塊的局部模糊類型。這也說(shuō)明了將該SVM分類模型應(yīng)用于圖像局部模糊區(qū)域檢測(cè)有一定的可行性與可靠性。在局部模糊檢測(cè)試驗(yàn)中,最終的檢測(cè)效果圖證明了本文提出的圖像局部模糊檢測(cè)的SVM方法具有較高的準(zhǔn)確率。對(duì)比于理想狀態(tài)下的局部模糊檢測(cè)效果圖,該局部模糊檢測(cè)算法能夠較為準(zhǔn)確的檢測(cè)到一幅局部模糊圖像中具體的模糊區(qū)域。
[Abstract]:Image plays a key role in the expression and transmission of information. The most commonly used tool for recording image information is the camera. When the image is photographed, the image distortion is unavoidable, such as blurred, color deviation, etc. and the image degradation will greatly reduce the accuracy of the image information expression. This paper first analyzes the transport of image information. Two local fuzzy formation mechanism of dynamic fuzzy and defocus fuzzy. Then the characteristics and differences of the two local fuzzy images are compared. The difference between the fuzzy and the clear images can be found at the same time from the study of the formation mechanism of the blurred image of the motion blur and the defocus. The method of estimating the fuzzy kernel of the image is not used to distinguish the fuzzy type, but four fuzzy features are used to distinguish fuzzy and clear, and to judge the fuzzy type of the image. Multiple fuzzy features are extracted as the feature data of the classification. It is to better express the information of the image from all aspects. All image blocks are studied and trained by SVM, and the prediction model of image type is established. Two SVM classifiers can be used to classify images into clear images, motion blurred images and blur blurred images. Finally, using the SVM classification model established above, the local blurred image is examined with each pixel as the central block. Determine whether it is fuzzy to complete the detection of the local blurred region of the image. Because of the phenomenon of misjudgement in the image detection, there will be a small hole or no connection in the final two valued effect map. Finally, the result of the final test is perfected by mathematical morphology algorithm. The local fuzzy classification test knot of image is used. The results show that the fuzzy feature proposed in this paper can accurately determine whether the image block is a fuzzy image, and can distinguish the local fuzzy type of the image block. It also shows that the application of the SVM classification model to the local fuzzy region detection is feasible and reliable. In the local fuzzy test, the final detection is tested. The test results show that the SVM method of local blurred image detection proposed in this paper has high accuracy. Compared with the local fuzzy detection effect map under ideal state, the local fuzzy detection algorithm can detect the specific fuzzy region in a local blurred image more accurately.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 咸兆勇;甘金明;玉振明;李陶深;;一種基于相關(guān)性和局部標(biāo)準(zhǔn)差的圖像失焦模糊區(qū)域檢測(cè)方法[J];計(jì)算機(jī)應(yīng)用與軟件;2014年09期

2 段新濤;宋黎明;孫印杰;;基于四向差分的局部模糊圖像檢測(cè)[J];電腦知識(shí)與技術(shù);2012年19期

3 范海菊;張玉珊;馮乃勤;;基于紋理濾波和方向微分的模糊方向識(shí)別[J];計(jì)算機(jī)工程;2012年06期

4 吳昊;方賢勇;羅斌;賀彪;;圖像局部模糊的自動(dòng)檢測(cè)方法[J];計(jì)算機(jī)工程;2011年18期

5 宋暉;薛云;張良均;;基于SVM分類問題的核函數(shù)選擇仿真研究[J];計(jì)算機(jī)與現(xiàn)代化;2011年08期

6 田巖;劉繼軍;謝玉波;史文中;;基于局部模糊方差的過渡區(qū)提取及圖像分割[J];紅外與毫米波學(xué)報(bào);2007年05期

7 劉遠(yuǎn)航 ,劉文開;數(shù)碼相機(jī)原理簡(jiǎn)論[J];照相機(jī);2002年07期

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