【摘要】：二維經(jīng)驗?zāi)Ｊ椒纸馐屈S變換經(jīng)驗?zāi)Ｊ椒纸夥椒ǖ亩S拓展,它具有良好的自適應(yīng)性處理能力,為滿足圖像處理中非平穩(wěn)信號特征分析和礦山企業(yè)的實際需要提供了新的技術(shù)措施。然而,實際應(yīng)用發(fā)現(xiàn),它存在插值優(yōu)化、端部效應(yīng)、停止條件以及圖像融合時分解圖像的不一致而較難融合等問題。為解決這些問題,本文開展了深入系統(tǒng)的研究,涉及到如下四個方面的工作。(1)提出了一種基于粒子群分形的插值算法。利用分形布朗函數(shù)理論對圖像分析,得到圖像特征量,再進行自適應(yīng)插值計算。該算法不僅可以提高二維經(jīng)驗?zāi)Ｊ椒纸獾牟逯稻群托?實現(xiàn)圖像快速分解,還為其它幾個問題的解決奠定了良好的基礎(chǔ)。(2)提出了一種基于自適應(yīng)支持向量機延拓和鏡像閉合技術(shù)相結(jié)合的端部效應(yīng)處理算法。通過自適應(yīng)支持向量機延拓圖像,得到圖像邊緣極值點,對延拓圖像進行鏡像閉合處理,實施圖像分解。相關(guān)圖像的二維經(jīng)驗?zāi)Ｊ椒纸饨Y(jié)果表明,這一算法的應(yīng)用可以減弱甚至消除二維經(jīng)驗?zāi)Ｊ椒纸膺^程中的端部效應(yīng),從而較好地保證圖像邊緣信息及細節(jié)信息的有效提取。(3)建立了一種基于篩分曲面在零值平面上投影位置不重合極值點數(shù)目及其變化速度的停止條件。先對圖像在二維經(jīng)驗?zāi)Ｊ椒纸膺^程中得到的包絡(luò)曲面進行分析,再對分解過程中極值點的演化規(guī)律進行跟蹤,最后通過不斷篩分得到曲面空間位置信息判斷篩分停止條件。它使圖像進行二維經(jīng)驗?zāi)Ｊ椒纸獾玫降亩S固有模態(tài)函數(shù)更趨于圖像本身特性,能有效消除圖像的過度分解和欠分解現(xiàn)象。(4)建立了基于二維經(jīng)驗?zāi)Ｊ椒纸?SIFT圖像特征提取新算法與基于二維經(jīng)驗?zāi)Ｊ椒纸?極值點協(xié)調(diào)圖像融合新算法。前者利用二維經(jīng)驗?zāi)Ｊ椒纸獾玫蕉鄠�二維固有模態(tài)函數(shù),提取其特征后累加合成。該算法可以避免不同模態(tài)信息間的相互干擾,有利于更快更準(zhǔn)地獲得圖像各類特征信息。后者基于多幅圖像二維經(jīng)驗?zāi)Ｊ椒纸獾玫綐O大值點集和極小值點集,進行多幅圖像自適應(yīng)基函數(shù)的協(xié)同操作,使其得到共同自適應(yīng)基函數(shù),再對圖像分解得到的二維固有模態(tài)函數(shù)分別融合并合成累加得到融合圖像,由此構(gòu)造了一種具有自適應(yīng)特性的協(xié)調(diào)操作圖像融合算法。研究結(jié)果表明該算法在獲得更多原始圖像細節(jié)、邊緣信息以及提高融合圖像質(zhì)量方面具有良好的效果。大量圖像處理實例證實了所提出技術(shù)方法具有良好的應(yīng)用效果。綜上所述,本文的研究工作是對二維經(jīng)驗?zāi)Ｊ椒纸庠趫D像處理中應(yīng)用的發(fā)展與完善,也為圖像處理中非平穩(wěn)信號特征分析提供了有益的基礎(chǔ)性技術(shù)措施。同時,它也是自適應(yīng)數(shù)字圖像處理方法在礦山安全監(jiān)控的應(yīng)用拓展。
[Abstract]:The two-dimensional empirical mode decomposition is a two-dimensional extension of the yellow transform empirical mode decomposition method, and it has good adaptive processing ability. New technical measures are provided to satisfy the characteristics analysis of non-stationary signals in image processing and the practical needs of mining enterprises. However, it is found that it has many problems, such as interpolation optimization, end effect, stop condition and the inconsistency of decomposing image in image fusion, which makes it difficult to fuse. In order to solve these problems, this paper has carried out in-depth and systematic research, involving the following four aspects of work. (1) an interpolation algorithm based on particle swarm fractal is proposed. The fractal Brownian function theory is used to analyze the image, get the feature quantity of the image, and then carry on the adaptive interpolation calculation. This algorithm can not only improve the interpolation accuracy and efficiency of 2-D empirical mode decomposition, but also realize the fast image decomposition. It also lays a good foundation for solving other problems. (2) an end effect processing algorithm based on the combination of adaptive support vector machine extension and mirror image closure technique is proposed. By using adaptive support vector machine (SVM) to extend the image, the extreme points of the edge of the image are obtained, and the image closure is processed and the image decomposition is carried out. The results of two-dimensional empirical mode decomposition of correlated images show that the application of this algorithm can weaken or even eliminate the end effect in the process of two-dimensional empirical mode decomposition. Thus, the edge information and detail information of the image can be extracted effectively. (3) A stopping condition based on the number of non-coincident extreme points in the projection position of the screen surface on the zero-valued plane and its changing speed is established. Firstly, the enveloping surface of the image in the process of two-dimensional empirical mode decomposition is analyzed, and then the evolution rule of the extreme point in the decomposition process is tracked. Finally, the space position information of the surface is continuously screened to judge the screening stop condition. It makes the two-dimensional intrinsic mode function obtained from the two-dimensional empirical mode decomposition of the image tend to the characteristics of the image itself. It can effectively eliminate the phenomenon of over-decomposition and under-decomposition. (4) A new image fusion algorithm based on two-dimensional empirical mode decomposition-SIFT image feature extraction and two-dimensional empirical mode decomposition-extreme point coordination image fusion is proposed. The former uses two-dimensional empirical mode decomposition to obtain multiple two-dimensional intrinsic modal functions, and then extracts their features and then accumulates them. This algorithm can avoid the mutual interference between different modal information, and it is helpful to get all kinds of image feature information more quickly and accurately. Based on the two-dimensional empirical mode decomposition of multiple images, the maximum point set and the minimum point set are obtained, and the cooperative operation of multiple image adaptive basis functions is carried out, so that the common adaptive basis functions can be obtained from the two-dimensional empirical mode decomposition of multiple images. Then the two-dimensional intrinsic modal functions obtained by image decomposition are fused and synthesized to get the fused image, and then an adaptive image fusion algorithm with coordinated operation is proposed. The results show that the algorithm has a good effect on obtaining more details of original image, edge information and improving the quality of fusion image. A large number of image processing examples show that the proposed technique has a good application effect. To sum up, the research work of this paper is to develop and perfect the application of two-dimensional empirical mode decomposition in image processing, and to provide useful basic technical measures for the analysis of non-stationary signal features in image processing. At the same time, it is also an extension of the application of adaptive digital image processing method in mine safety monitoring.
【學(xué)位授予單位】：北京科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前2條

1 湯雅妃;張云勇;張尼;;基于指紋識別的云安全認證技術(shù)[J];電信科學(xué);2015年08期

2 鄧?yán)?胡小林;李鋒;湯寶平;;基于支持向量機的BS-EMD端點效應(yīng)消除方法[J];振動.測試與診斷;2011年03期

，

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