本文選題：運動圖像的盲復原　切入點：邊緣檢測　出處：《北京交通大學》2017年碩士論文

【摘要】：運動模糊圖像的復原問題一直是圖像復原領域研究的熱點。在照片的拍攝過程中,由于相機與物體之間產(chǎn)生相對位移,導致發(fā)生運動模糊,嚴重影響到圖像的后續(xù)處理與應用。圖像去模糊就是采用一定的手段從模糊圖像中恢復出清晰的圖像,而在模糊核未知的情況下的復原問題就屬于圖像的盲復原范疇,這屬于一種嚴重的病態(tài)問題。隨著研究的不斷深入,研究者提出很多優(yōu)秀的算法,解決了圖像盲復原的難題,但是許多現(xiàn)有的算法存在著一些不足,主要就是由于圖像邊緣提取和模糊核估計不準確導致的振鈴效應以及圖像細節(jié)信息丟失的問題。針對這些問題,本文從邊緣提取和模糊核估計方面提出了一些改進算法,來改善運動模糊圖像的盲復原效果。首先,本文提出了改進的Canny算子邊緣提取算法。通過研究快速盲去模糊的算法,利用邊緣檢測算子進行邊緣提取,再根據(jù)圖像邊緣信息的先驗知識來估計模糊核。通過對邊緣提取算法的研究,提出自適應的閾值設置方法,來設置高低閾值,濾除虛假邊緣以及孤立的邊緣點。實驗結果表明該算法能夠濾除大量的偽邊緣,使得到的邊緣更清晰、更連續(xù)。其次,本文提出了對估計的初始模糊核的細化方法。該方法通過邊緣檢測,估計模糊核,然后采用迭代支持域檢測算法對模糊核進行修正細化,抑制初始模糊核中的一些離散值,使得到的模糊核更加精細、更加準確,采用超拉普拉斯分布擬合圖像梯度分布,并且利用圖像金字塔進行多尺度恢復,解決了模糊核過大的問題。實驗結果表明該算法可以有效抑制初始模糊核中的離散值,獲得較理想的模糊核,能有效地減弱復原圖像的振鈴效應以及能夠保留大量的細節(jié)信息。
[Abstract]:The problem of motion blur image restoration has been a hot topic in the field of image restoration.In the process of photography, the relative displacement between the camera and the object results in motion blur, which seriously affects the subsequent processing and application of the image.Image de-blurring is to restore the clear image from the blurred image by certain means, but the restoration problem in the case of unknown fuzzy kernel belongs to the category of blind image restoration, which belongs to a serious pathological problem.With the deepening of the research, researchers put forward many excellent algorithms to solve the problem of blind image restoration, but many existing algorithms have some shortcomings.It is mainly caused by ringing effect caused by image edge extraction and inaccuracy of fuzzy kernel estimation and the loss of image detail information.To solve these problems, this paper proposes some improved algorithms for edge extraction and fuzzy kernel estimation to improve the blind restoration of motion blur images.Firstly, an improved edge detection algorithm for Canny operator is proposed.By studying the fast blind de-blurring algorithm, the edge detection operator is used to extract the edge, and then the fuzzy kernel is estimated according to the prior knowledge of the image edge information.Based on the research of edge extraction algorithm, an adaptive threshold setting method is proposed to set high and low thresholds and filter false edges and isolated edge points.Experimental results show that the algorithm can filter a large number of pseudo-edges and make the edges clearer and more continuous.Secondly, a refinement method for the initial fuzzy kernel of the estimation is proposed.In this method, the fuzzy kernel is estimated by edge detection, then the fuzzy kernel is refined by iterative support domain detection algorithm, and some discrete values in the initial fuzzy kernel are suppressed, which makes the fuzzy kernel more precise and accurate.The super-Laplace distribution is used to fit the image gradient distribution, and the image pyramid is used for multi-scale restoration, which solves the problem that the fuzzy kernel is too large.The experimental results show that the proposed algorithm can effectively suppress the discrete values in the initial fuzzy kernel, obtain the ideal fuzzy kernel, effectively attenuate the ringing effect of the restored image and retain a large amount of detail information.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：1701151