【摘要】：隨著人類對(duì)微觀世界的觀察需求,顯微鏡被廣泛應(yīng)用,但是傳統(tǒng)顯微鏡在使用過程中有諸多不便,如對(duì)焦緩慢、景深限制等。基于圖像序列的超景深圖像融合算法,是根據(jù)采集于同一場景但部分對(duì)焦清晰的圖像序列,合成得到一副全局對(duì)焦清晰的超景深圖像。這樣的算法可以打破傳統(tǒng)光學(xué)顯微鏡的景深限制,設(shè)計(jì)出方便實(shí)用的超景深顯微鏡。超景深顯微鏡在放大倍數(shù)較高的情況下能方便清晰地觀察起伏較大的樣品,在醫(yī)療、生物、工業(yè)生產(chǎn)等領(lǐng)域廣泛應(yīng)用。故本文研究了多種圖像融合算法,如基于圖像金字塔的圖像融合算法、基于小波變換的圖像融合算法和基于最優(yōu)化的圖像融合算法,并又提出一種高效的景深擴(kuò)展算法用于超景深顯微鏡設(shè)計(jì)。首先,在本文提出的方法中,通過設(shè)計(jì)一個(gè)根據(jù)圖像紋理和分辨率自適應(yīng)的聚焦度量用于檢測圖像序列中所有圖像的所有位置的聚焦清晰程度。為了增強(qiáng)系統(tǒng)魯棒性,針對(duì)顯微鏡光學(xué)特點(diǎn),把對(duì)焦度量結(jié)果用一個(gè)高斯分布表示,用以模擬還原真實(shí)場景的深度信息和對(duì)焦?fàn)顩r。然后,為了降低噪聲和特殊樣品對(duì)融合結(jié)果的影響,以便適應(yīng)更多的應(yīng)用場景,本文的方法將圖像中每個(gè)像素根據(jù)不同的聚焦情況分為三種類型,并針對(duì)不同的聚焦特點(diǎn)使用不同的融合規(guī)則。這里稱這個(gè)步驟為局部加權(quán)平均的融合規(guī)則。在圖像區(qū)域清晰且噪聲少的情況下僅僅使用一張圖像執(zhí)行融合;在圖像區(qū)域含有噪聲的情況下使用兩張或者三張比較清晰的圖像執(zhí)行融合;在信息質(zhì)量差的圖像區(qū)域使用附近區(qū)域的規(guī)則替代當(dāng)前位置的融合規(guī)則。最后,為了評(píng)估本文所提出算法的有效性,本文在大量模擬數(shù)據(jù)和真實(shí)數(shù)據(jù)上進(jìn)行廣泛的實(shí)驗(yàn),最終的定量與定性結(jié)果表明本文提出的景深圖像融合算法是簡單且極其有效的。此外,大量的對(duì)比算法定量分析證明了本文所提出的算法無論是效果上還是速度上都遠(yuǎn)優(yōu)于傳統(tǒng)的方法。值得關(guān)注的是,一臺(tái)利用本文提出的算法的超景深數(shù)字光學(xué)顯微鏡被實(shí)際設(shè)計(jì)出來并已經(jīng)投入工業(yè)界使用,對(duì)實(shí)際工業(yè)樣品的真實(shí)顯示結(jié)果令客戶滿意,且獲得了大額的經(jīng)濟(jì)效益。
[Abstract]:Microscopes are widely used with the need of human being to observe the microcosm, but the traditional microscopes have many inconveniences, such as slow focusing, limited depth of field and so on. The fusion algorithm of hyper-depth image based on image sequence is based on the image sequence which is collected in the same scene but partially focused, and a set of hyper-depth image with global focus is synthesized. This algorithm can break through the limit of depth of field of traditional optical microscope and design a convenient and practical hyper-depth microscope. The hyperfield depth microscope can be used in many fields such as medical treatment, biology, industrial production and so on, which can easily and clearly observe the large undulating samples under the condition of high magnification. So this paper studies many image fusion algorithms, such as image fusion algorithm based on image pyramid, image fusion algorithm based on wavelet transform and image fusion algorithm based on optimization. An efficient depth of field extension algorithm is also proposed for the design of hyper-depth microscope. Firstly, in the proposed method, an adaptive focusing metric based on image texture and resolution is designed to detect the focus clarity of all the images in the image sequence. In order to enhance the robustness of the system, aiming at the optical characteristics of the microscope, the focus measurement results are represented by a Gao Si distribution, which is used to simulate the depth information and the focusing state of the real scene. Then, in order to reduce the effect of noise and special samples on the fusion results, in order to adapt to more application scenarios, each pixel in the image is divided into three types according to different focusing conditions. According to different focusing characteristics, different fusion rules are used. This step is referred to here as the fusion rule of local weighted average. When the image area is clear and the noise is low, only one image is used to perform fusion, and two or three clear images are used to perform fusion when the image region contains noise. The fusion rule of the current location is replaced by the rule of the nearby region in the image area with poor information quality. Finally, in order to evaluate the effectiveness of the proposed algorithm, extensive experiments are carried out on a large number of simulated and real data. The final quantitative and qualitative results show that the proposed algorithm is simple and extremely effective. In addition, a large number of quantitative analysis results show that the proposed algorithm is far superior to the traditional method both in effect and speed. It is worth noting that a digital optical microscope using the algorithm proposed in this paper has been designed and put into use in industry. The actual display results of the actual industrial samples are satisfactory to the customers. And obtained the economic benefit of large amount.
【學(xué)位授予單位】：安徽大學(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 王莉;蔣洪;孫麗麗;;顯微鏡的發(fā)展綜述[J];科技信息;2009年11期

2 屈小波;閆敬文;謝國富;朱自謙;陳本剛;;A novel image fusion algorithm based on bandelet transform[J];Chinese Optics Letters;2007年10期

，

本文編號(hào)：2209347