發(fā)布時間：2019-06-21 10:47

【摘要】：在圖像獲得的過程中,由于成像條件和外界環(huán)境的干擾,往往會使圖像質量下降,但是在實際生活中,又需要高清晰的圖像。因此,有必要從退化的圖像中恢復出高清晰,高質量的圖像,這就是圖像復原的首要任務。另一方面,圖像復原作為一種底層圖像處理技術,在恢復出高質量圖像的同時,也給后續(xù)圖像處理奠定了質量基礎,因此圖像復原已成為圖像處理研究領域關注的基本問題。圖像復原是指應用圖像的先驗信息,建立合適的模型,從退化的圖像中恢復和重建原始圖像的一種圖像處理技術。它主要包括圖像去噪,圖像去模糊,圖像修復和提高分辨率幾個方面的問題。本文主要圍繞圖像去噪和圖像去模糊兩個方面做了深入研究。 論文首先對圖像復原的研究背景與意義,圖像退化模型,圖像復原的一般方法進行了簡單的分析與總結。 然后,基于全變分正則化在圖像復原的過程中會產生階梯效應這一現(xiàn)象,本文將圖像的稀疏先驗信息引入圖像復原問題中,設計了一種新的圖像復原模型。在本文設計的圖像模型中,由于緊小波框架可以有效的對逐段光滑函數(shù)進行稀疏表示和自適應獲得當前待處理圖像的多尺度邊緣結構,本文設計的模型可以有效地克服全變分正則化帶來的階梯效應。此外,由于本文模型的不光滑性,我們采用增廣拉格朗日算法進行數(shù)值求解。實驗結果證明,針對高斯噪聲情況下的圖像模糊問題,本文設計的模型可以有效復原圖像,并消除階梯效應,且復原效果優(yōu)于現(xiàn)有的圖像復原方法。 一般情況下,研究學者主要考慮了受高斯噪聲污染的圖像去模糊問題,但是在圖像成像的過程中,往往也會受到脈沖噪聲的污染,因此,我們考慮了受脈沖噪聲污染的圖像去模糊問題。由于脈沖噪聲的成像機理與高斯噪聲成像機理不一樣,所以針對高斯噪聲污染的圖像去模糊問題的方法不能直接用于受脈沖噪聲污染的圖像去模糊問題中,鑒于此,本文結合緊小波框架和全變分正則化設計了一種新的圖像復原模型,并采用增廣拉格朗日算法對其求解。實驗結果證明,本文設計的方法可以有效地處理脈沖噪聲污染的圖像去模糊問題。 最后基于自適應字典的稀疏表示方法可以有效地去除高斯噪聲,但是卻不能很好地去除脈沖噪聲。因此,針對受脈沖噪聲污染的圖像去噪問題,本文在自適應字典稀疏表示的基礎上設計了一種二階段脈沖噪聲去除方法。首先利用中值類型濾波器將圖像分為噪聲點和非噪聲點,然后建立基于l1-l1最小化的字典學習方法,并采用交替方向方法進行數(shù)值求解。實驗結果證明,本文提出的方法在有效去除噪聲的同時可以很好地保存圖像信息。
[Abstract]:In the process of image acquisition, due to the interference of the imaging conditions and the external environment, the image quality is often reduced, but high-definition images are required in the real life. Therefore, it is necessary to recover high-definition and high-quality images from the degraded image, which is the primary task of image restoration. On the other hand, image restoration, as a bottom-layer image processing technique, provides a quality base for subsequent image processing while restoring high-quality images, so that image restoration has become a basic problem in the field of image processing research. Image restoration is an image processing technique for restoring and reconstructing an original image from a degraded image by using the prior information of the image, establishing a suitable model, and recovering and reconstructing the original image from the degraded image. It mainly includes image de-noising, image de-blurring, image restoration and resolution. This paper mainly studies the two aspects of image de-noising and image de-blurring. In this paper, the background and significance of image restoration, the image degradation model and the general method of image restoration are analyzed in this paper. In this paper, we design a new image complex by introducing the sparse prior information of the image into the image restoration problem based on the phenomenon of the step effect in the process of image restoration based on the full-variation regularization. In the image model designed in this paper, because the compact wavelet frame can effectively perform the sparse representation of the piecewise smooth function and the multi-scale edge structure of the current image to be processed, the model designed in this paper can effectively overcome the order of the full-variational regularization. Ladder effect. In addition, due to the inhomogeneity of the model, we use the augmented Lagrangian algorithm to count. The experimental results show that the model designed in this paper can effectively recover the image and eliminate the step effect, and the recovery effect is better than that of the existing image. In general, the research scholars mainly take into account the image de-blurring problem of the Gaussian noise pollution, but in the process of image forming, the pollution of the impulse noise is often also affected, so we take into account the image of the pulse noise pollution As the imaging mechanism of the impulse noise is different from the Gaussian noise imaging mechanism, the method of image de-blurring for the Gaussian noise pollution cannot be directly used for the image de-blurring problem of the pulse noise pollution. In this paper, in view of this, a new image restoration model is designed by combining the compact wavelet frame and the full-variational regularization, and the augmented Lagrangian calculation is used. The experimental results show that the method designed in this paper can effectively deal with the image of pulse noise pollution. Finally, based on the sparse representation of the adaptive dictionary, the Gaussian noise can be effectively removed, but it can't be very good. To remove the impulse noise, a two-phase pulse is designed on the basis of the sparse representation of the adaptive dictionary for the image de-noising problem of the impulse noise pollution. the method comprises the following steps of: firstly, dividing an image into a noise point and a non-noise point by using a median type filter, and then establishing a dictionary learning method based on the l1-l1 minimization, The results of the experiment show that the method proposed in this paper can be very good at the same time.
【學位授予單位】：武漢大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TP391.41

【參考文獻】

相關期刊論文 前1條

1 ;FRAMELET BASED DECONVOLUTION[J];Journal of Computational Mathematics;2010年03期

相關博士學位論文 前1條

1 劉且根;基于增廣拉格朗日的字典學習算法及其在醫(yī)學成像和圖像處理中的應用[D];上海交通大學;2012年

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本文編號：2504018