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】：圖像在信息的表達與傳遞中有著關(guān)鍵的作用,人們最常用來記錄圖像信息的工具就是相機。當相機拍攝圖像時不可避免會存在圖像失真的情況,比如模糊、色偏等。而圖像退化后會大大地降低圖像信息表達的精準度。本文首先分析了運動模糊與失焦模糊兩種局部模糊的形成機制。然后對比兩種局部模糊圖像的退化機理,尋找兩者的特點與區(qū)別。從研究運動模糊與失焦模糊圖像的形成機理中,也可同時發(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地計算,所以本文未采用估計圖像模糊核的方法來區(qū)分模糊的類型。而是提出了四項模糊特征用于區(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。在圖像庫中,對所有的圖像塊采用SVM進行學(xué)習(xí)與訓(xùn)練,建立圖像類型的預(yù)測模型。利用兩次SVM分類器可將圖像分類為清晰圖像、運動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,對局部模糊圖像以每個像素為中心分塊進行檢測,判斷其是否為模糊,完成圖像局部模糊區(qū)域的檢測。由于圖像檢測存在誤判現(xiàn)象,所以最終檢測得到的二值化效果圖中會出現(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)算法對最終的試驗效果進行完善。圖像局部模糊分類試驗結(jié)果證明了本文提出的模糊特征能夠較為準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。這也說明了將該SVM分類模型應(yīng)用于圖像局部模糊區(qū)域檢測有一定的可行性與可靠性。在局部模糊檢測試驗中,最終的檢測效果圖證明了本文提出的圖像局部模糊檢測的SVM方法具有較高的準確率。對比于理想狀態(tài)下的局部模糊檢測效果圖,該局部模糊檢測算法能夠較為準確的檢測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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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