本文選題：多聚焦圖像融合　切入點(diǎn)：多尺度鄰域技術(shù)　出處：《昆明理工大學(xué)》2017年碩士論文

【摘要】：由于光學(xué)鏡頭景深具有的局限性,大部分的透鏡成像設(shè)備在獲取場(chǎng)景圖像時(shí)無法使所有物體都清晰成像。但是,在某些情況下,人們往往需要得到一幅在一個(gè)場(chǎng)景中所有物體都聚焦的清晰圖像。在上述背景下,多聚焦圖像融合技術(shù)引起了學(xué)者們廣泛的關(guān)注。隨著科學(xué)技術(shù)的飛速發(fā)展,多聚焦圖像融合已經(jīng)在醫(yī)學(xué)成像,計(jì)算機(jī)視覺,模式識(shí)別技術(shù),軍事圖像等一系列重要的任務(wù)中發(fā)揮著舉足輕重的作用。多聚焦圖像融合算法大致分為兩種方法:基于變換域的融合法和空間域融合法�；诳臻g域的方法大致分為為基于塊、基于區(qū)域和基于聚焦區(qū)域檢測(cè)的方法�；诰劢箙^(qū)域檢測(cè)的方法最為受到學(xué)者的關(guān)注,但是由于圖像的復(fù)雜性,在聚焦區(qū)域和散焦區(qū)域之間,由于許多圖像沒有明確的紋理和明確的邊界,聚焦區(qū)域的邊界會(huì)存在一條明顯的邊界接縫。在基于變換域的方法中,最具有代表性的方法就是多尺度融合方法。在多尺度變換對(duì)多聚焦圖像進(jìn)行融合過程中發(fā)現(xiàn),不同子帶的融合規(guī)則是影響圖像融合效果的一個(gè)關(guān)鍵性的因素。但是,近年來研究者提出的新的融合規(guī)則下對(duì)圖像進(jìn)行融合,融合的結(jié)果效果并沒有顯著的提升。為了解決上述問題,本文主要貢獻(xiàn)如下:(1)提出了基于多尺度、多方向領(lǐng)域距離(MMND)的兩種融合規(guī)則,這兩種融合規(guī)則都分別實(shí)現(xiàn)了對(duì)多聚焦圖像融合結(jié)果性能的提升。提出此方法的理論依據(jù)是:低質(zhì)量融合圖像和源圖像的差異比高質(zhì)量融合圖像和源圖像的差異更加明顯。(2)基于上述理論依據(jù),在優(yōu)化融合圖像過程中,源圖像上的像素點(diǎn)被分為三種,聚焦區(qū)域像素點(diǎn)、離焦區(qū)域像素點(diǎn),和過渡區(qū)域像素點(diǎn)�；谠磮D像的三種像素點(diǎn),就可以對(duì)融合圖像的結(jié)果在基于MMND變換域融合規(guī)則與MMND空間域融合規(guī)則上通過差異圖構(gòu)造出的決策圖進(jìn)行圖像的優(yōu)化提升。本文經(jīng)過大量的實(shí)驗(yàn)結(jié)果證明,本文提出的兩個(gè)融合規(guī)則比一些近年來提出的先進(jìn)的算法表現(xiàn)更加優(yōu)異。
[Abstract]:Because of the limitations of the depth of field of the optical lens, most lens imaging devices are unable to get a clear image of all objects when they get the scene image. However, in some cases, People often need to get a clear image in which all objects are focused in one scene. In the above background, multi-focus image fusion technology has attracted wide attention of scholars. With the rapid development of science and technology, Multi-focus image fusion has been used in medical imaging, computer vision, pattern recognition technology, The multi-focus image fusion algorithm is divided into two kinds of methods: the fusion method based on transform domain and the method based on space domain, and the method based on space domain is roughly divided into blocks. The methods based on region and focus region are the most concerned by scholars, but because of the complexity of image, they are between the focus region and defocus region. Because many images have no clear texture and boundary, there is an obvious boundary seam in the boundary of the focus region. The most representative method is multi-scale fusion. In the process of multi-scale image fusion with multi-scale transformation, it is found that the fusion rules of different sub-bands are a key factor affecting the image fusion effect. In recent years, the new fusion rules proposed by researchers have not significantly improved the result of image fusion. In order to solve the above problems, the main contributions of this paper are as follows: 1) based on multi-scale. Two kinds of fusion rules for multidirectional domain distance (MMND), These two fusion rules can improve the performance of multi-focus image fusion respectively. The theoretical basis of this method is that the difference between low quality fusion image and source image is higher than that between high quality fusion image and source image. Based on the above theoretical basis, In the process of optimizing the fusion image, the pixel points on the source image are divided into three types: the focus region pixel, the defocus region pixel point, and the transition region pixel point, which are based on the three pixel points of the source image. The result of image fusion can be optimized by using the decision graph constructed by difference graph on the basis of MMND transform domain fusion rule and MMND space domain fusion rule. The two fusion rules proposed in this paper are better than some advanced algorithms proposed in recent years.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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本文編號(hào)：1671696