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  本文選題：圖像分解 + 雙邊濾波　； 參考：《山東師范大學》2017年碩士論文

【摘要】：圖像分解在計算機視覺領域中一直是一個被廣泛關注的問題,該問題的研究目標是將一幅圖像分解成若干個不同的分量,從而實現(xiàn)原圖像中的主要結構與紋理細節(jié)等信息的分離。解決這一問題,對計算機視覺和醫(yī)學圖像等領域的許多工作具有重要意義。然而,一方面,現(xiàn)存的經典圖像分解方法大多缺少相應的函數(shù)來定義某些復雜的結構特征,如經典的雙邊濾波(BF)和雙邊紋理濾波(BTF)缺少定義視網膜圖像血管結構特征的函數(shù)。另一方面,這些方法忽視了圖像中常見的偏場信息對圖像造成的影響,分解出的信息常受到偏場的干擾而嚴重丟失。針對這些問題,本文提出了圖像結構、紋理和偏場協(xié)同分解的模型,并基于圖像管狀結構、紋理的分解以及對圖像偏場的估計提出了圖像協(xié)同分解的方法。該方法能夠在分解圖像中復雜的管狀結構與紋理細節(jié)的同時不受偏場信息的影響。具體地,本文提出了一個最優(yōu)線性擴散函數(shù)(OLSF)空間核算子來提取管狀結構的特征,然后將其與BTF中的紋理分解算子Patch-shift(PS)融合,用于有效分解圖像中的管狀結構與紋理。為了消除偏場信息的干擾,我們利用魯棒性較強的圖像梯度分布稀疏性來有效地估計圖像的偏場信息。具體來講,本文的研究和貢獻主要有以下幾點:(1)提出了一個圖像管狀結構、細節(jié)分解方法,并將其成功地應用于眼底圖像的降噪任務。該方法利用OLSF有效地提取特殊管狀結構特征,例如局部血管的方向、尺度等等,然后利用這些特征區(qū)分血管結構信息和背景細節(jié)信息,最終在降低圖像中的噪聲的同時極大的保留血管結構。大量的手工圖像和視網膜圖像的實驗結果表面,在保留對比度較低的細血管的效果上,該方法要優(yōu)于經典的BF方法。此外,該方法不僅為分解視網膜圖像中的血管結構提供了可行性,并且在其他包含小尺度的、低對比度的管狀結構的圖像上同樣有效,為下一步的管狀結構、紋理分解工作提供了基礎。(2)提出了一個圖像管狀結構、紋理分解方法,能夠有效地分解圖像的管狀和紋理細節(jié)信息。該方法基于BF框架,融合了PS算子和提出的OLSF。其中,PS算子利用每個像素的局部統(tǒng)計特征來定義該像素的紋理特征,具有很好的圖像的結構和紋理細節(jié)分解效果。大量的視網膜圖像和自然圖像的實驗結果表明,利用PS算子和OLSF定義BF的濾波核,能夠在消除紋理信息的同時有效地保護管狀結構,且其效果要優(yōu)于經典的BF和BTF圖像分解方法。(3)提出了圖像結構、紋理和偏場協(xié)同分解的模型,并在圖像管狀結構、紋理分解的基礎上加入了圖像偏場的估計,提出了圖像協(xié)同分解模型的實現(xiàn)方法。本文利用了圖像梯度分布的稀疏性估計圖像偏場信息,同時結合管狀結構-紋理濾波分解方法,最終將圖像分解成管狀結構、背景紋理和偏場三個分量。大量的自然圖像和眼底圖像的實驗對比的結果表面,圖像協(xié)同分解模型比傳統(tǒng)模型更加嚴謹,且實用性更強。和現(xiàn)存的經典的BF和BTF圖像分解方法更相比,圖像協(xié)同分解方法的優(yōu)勢在與分解紋理信息的同時能夠更好的保護圖像管狀結構,而不會受到偏場信息的影響。
[Abstract]:Image decomposition has been a widespread concern in the field of computer vision. The goal of this problem is to decompose an image into several different components, so as to separate the information such as the main structure and the texture details in the original image. To solve this problem, many fields such as computer vision and medical images are solved. However, on the one hand, the existing classical image decomposition methods mostly lack the corresponding functions to define some complex structural features, such as the classical bilateral filtering (BF) and bilateral texture filtering (BTF) lack of functions to define the vascular structural features of the retinal images. On the other hand, these methods ignore the common image in the image. In this paper, the model of image structure, texture and partial field synergetic decomposition is proposed. Based on the image tube structure, the decomposition of texture and the estimation of image partial field, the method of image synergetic decomposition is proposed. In this paper, an optimal linear diffusion function (OLSF) space accounting is proposed to extract the characteristics of the tubular structure, and then it is fused with the texture decomposition Patch-shift (PS) in the BTF to effectively decompose the image. In order to eliminate the interference of partial field information, we use robust image gradient sparsity to effectively estimate the partial field information of images. In particular, the research and contribution of this paper are as follows: (1) an image tubular structure, detail decomposition method is proposed, and it is applied successfully to the image. This method uses OLSF to effectively extract special tubular structure features, such as the direction of the local blood vessel, the scale and so on, and then use these features to distinguish the vascular structure information and the background details, and eventually reduce the noise in the image and retain the vascular structure greatly. A large number of manual images and network. The experimental results surface of the membrane image is better than the classical BF method in preserving the low contrast fine blood vessel. In addition, this method not only provides the feasibility to decompose the vascular structure in the retinal image, but also is effective in the other small scale, low contrast tubular structure images, for the next step. The tubular structure provides the basis for the texture decomposition. (2) an image tubular structure and a texture decomposition method are proposed, which can effectively decompose the tube and texture details of the image. Based on the BF framework, the PS operator and the proposed OLSF. are fused, and the PS operator uses the local statistical features of each pixel to define the pixel. Texture features, with good image structure and texture details decomposition effect. A large number of experimental results of retinal images and natural images show that using the PS operator and OLSF to define the filter kernel of BF can effectively protect the texture information and effectively protect the tubular structure, and its effect is better than the classical BF and BTF image decomposition methods. (3) The model of synergetic decomposition of image structure, texture and partial field is proposed, and the estimation of image partial field is added on the basis of image tube structure and texture decomposition. The realization method of image synergetic decomposition model is proposed. This paper uses the sparsity of image gradient distribution to estimate image partial field information and combines tubular texture filtering. Finally, the image is decomposed into a tubular structure, a background texture and a partial field of three components. A large number of natural images and eye images are compared with the results of the experiment. The image co decomposition model is more rigorous and more practical than the traditional model. Compared with the existing classical BF and BTF image decomposition methods, the image cooperative decomposing side The advantage of the method is that it can better protect the image tube structure while decomposing the texture information without being affected by the bias field information.

【學位授予單位】：山東師范大學
【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：1858009