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

【摘要】：圖像分解在計(jì)算機(jī)視覺領(lǐng)域中一直是一個(gè)被廣泛關(guān)注的問題,該問題的研究目標(biāo)是將一幅圖像分解成若干個(gè)不同的分量,從而實(shí)現(xiàn)原圖像中的主要結(jié)構(gòu)與紋理細(xì)節(jié)等信息的分離。解決這一問題,對(duì)計(jì)算機(jī)視覺和醫(yī)學(xué)圖像等領(lǐng)域的許多工作具有重要意義。然而,一方面,現(xiàn)存的經(jīng)典圖像分解方法大多缺少相應(yīng)的函數(shù)來定義某些復(fù)雜的結(jié)構(gòu)特征,如經(jīng)典的雙邊濾波(BF)和雙邊紋理濾波(BTF)缺少定義視網(wǎng)膜圖像血管結(jié)構(gòu)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(duì)圖像造成的影響,分解出的信息常受到偏場的干擾而嚴(yán)重丟失。針對(duì)這些問題,本文提出了圖像結(jié)構(gòu)、紋理和偏場協(xié)同分解的模型,并基于圖像管狀結(jié)構(gòu)、紋理的分解以及對(duì)圖像偏場的估計(jì)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(fù)雜的管狀結(jié)構(gòu)與紋理細(xì)節(jié)的同時(shí)不受偏場信息的影響。具體地,本文提出了一個(gè)最優(yōu)線性擴(kuò)散函數(shù)(OLSF)空間核算子來提取管狀結(jié)構(gòu)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(jié)構(gòu)與紋理。為了消除偏場信息的干擾,我們利用魯棒性較強(qiáng)的圖像梯度分布稀疏性來有效地估計(jì)圖像的偏場信息。具體來講,本文的研究和貢獻(xiàn)主要有以下幾點(diǎn):(1)提出了一個(gè)圖像管狀結(jié)構(gòu)、細(xì)節(jié)分解方法,并將其成功地應(yīng)用于眼底圖像的降噪任務(wù)。該方法利用OLSF有效地提取特殊管狀結(jié)構(gòu)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(jié)構(gòu)信息和背景細(xì)節(jié)信息,最終在降低圖像中的噪聲的同時(shí)極大的保留血管結(jié)構(gòu)。大量的手工圖像和視網(wǎng)膜圖像的實(shí)驗(yàn)結(jié)果表面,在保留對(duì)比度較低的細(xì)血管的效果上,該方法要優(yōu)于經(jīng)典的BF方法。此外,該方法不僅為分解視網(wǎng)膜圖像中的血管結(jié)構(gòu)提供了可行性,并且在其他包含小尺度的、低對(duì)比度的管狀結(jié)構(gòu)的圖像上同樣有效,為下一步的管狀結(jié)構(gòu)、紋理分解工作提供了基礎(chǔ)。(2)提出了一個(gè)圖像管狀結(jié)構(gòu)、紋理分解方法,能夠有效地分解圖像的管狀和紋理細(xì)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(gè)像素的局部統(tǒng)計(jì)特征來定義該像素的紋理特征,具有很好的圖像的結(jié)構(gòu)和紋理細(xì)節(jié)分解效果。大量的視網(wǎng)膜圖像和自然圖像的實(shí)驗(yàn)結(jié)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(shí)有效地保護(hù)管狀結(jié)構(gòu),且其效果要優(yōu)于經(jīng)典的BF和BTF圖像分解方法。(3)提出了圖像結(jié)構(gòu)、紋理和偏場協(xié)同分解的模型,并在圖像管狀結(jié)構(gòu)、紋理分解的基礎(chǔ)上加入了圖像偏場的估計(jì),提出了圖像協(xié)同分解模型的實(shí)現(xiàn)方法。本文利用了圖像梯度分布的稀疏性估計(jì)圖像偏場信息,同時(shí)結(jié)合管狀結(jié)構(gòu)-紋理濾波分解方法,最終將圖像分解成管狀結(jié)構(gòu)、背景紋理和偏場三個(gè)分量。大量的自然圖像和眼底圖像的實(shí)驗(yàn)對(duì)比的結(jié)果表面,圖像協(xié)同分解模型比傳統(tǒng)模型更加嚴(yán)謹(jǐn),且實(shí)用性更強(qiáng)。和現(xiàn)存的經(jīng)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(shí)能夠更好的保護(hù)圖像管狀結(jié)構(gòu),而不會(huì)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(xué)位授予單位】：山東師范大學(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2條

1 何克抗;信息技術(shù)與課程整合的目標(biāo)與意義[J];教育研究;2002年04期

2 曹莉華,柳偉,李國輝;基于多種主色調(diào)的圖像檢索算法研究與實(shí)現(xiàn)[J];計(jì)算機(jī)研究與發(fā)展;1999年01期

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本文編號(hào)：1858009