本文選題：熒光顯微　切入點(diǎn)：點(diǎn)擴(kuò)散函數(shù)　出處：《華中科技大學(xué)》2016年碩士論文

【摘要】：熒光顯微成像方法是采用紫外線作為照射源,來(lái)照射被檢測(cè)細(xì)胞,使細(xì)胞結(jié)構(gòu)發(fā)出熒光,然后在顯微鏡下觀察細(xì)胞的形狀及其他特征。在生物醫(yī)學(xué)研究中,通過(guò)熒光成像技術(shù),可以查看到生物細(xì)胞的活體結(jié)構(gòu)和活動(dòng)機(jī)理。但是由于各種因素的干擾,成像系統(tǒng)得到的圖像分辨率都會(huì)降低,而解卷積的目的就是提高退化后圖像的分辨率。通常有兩種可用的解卷積方法:光學(xué)的和計(jì)算的。光學(xué)方法是在散焦光到達(dá)檢測(cè)器之前就阻擋它以減少失真。雖然光學(xué)處理的圖像的三維分辨率更為明顯,但是該圖像包含嚴(yán)重的各向異性。在計(jì)算解卷積的方法中,數(shù)據(jù)則是由計(jì)算機(jī)處理,以減弱圖像中的噪聲和模糊,本文就是通過(guò)計(jì)算的方法來(lái)提升圖像分辨率。本文提供了三種解卷積的思路。傳統(tǒng)解卷積方法均為非盲解卷積,僅適用于點(diǎn)擴(kuò)散函數(shù)已知的情形,我們?cè)O(shè)計(jì)了一種基于參數(shù)化點(diǎn)擴(kuò)散函數(shù)模型的解卷積方法,將點(diǎn)擴(kuò)散函數(shù)擬合為多重高斯疊加形式,構(gòu)造出了ER方法的盲解卷積形式,然后采用輪流優(yōu)化方法進(jìn)行求解,同時(shí)我們將結(jié)合TV與Hessian正則的多重正則方法用于ER解卷積。第二種思路是在似然項(xiàng)不變的情況下提出了Non-local正則項(xiàng),并應(yīng)用到二維空間圖像與三維時(shí)間序列圖像中,并采用最速下降法求解。思路三則是利用三維熒光圖像縱軸分辨率低于橫軸的特點(diǎn),將圖像沿縱軸進(jìn)行插值,以使與橫軸分辨率一致,然后采用稀疏表示的方法從橫軸學(xué)習(xí)字典應(yīng)用到縱軸,同時(shí)結(jié)合縱軸的Non-loal先驗(yàn)來(lái)提高縱軸的分辨率。本文采用MATLAB R2012a和Visual Studio 2012軟件進(jìn)行計(jì)算機(jī)仿真,并將每種方法的實(shí)驗(yàn)結(jié)果都與Richard-Lucy、Richard-Lucy+TV和維納濾波方法進(jìn)行了對(duì)比,并對(duì)解卷積結(jié)果針對(duì)峰值信噪比(PSNR)、結(jié)構(gòu)相似度(SSIM)兩個(gè)性能指標(biāo)進(jìn)行了比較和分析。通過(guò)對(duì)仿真數(shù)據(jù)和真實(shí)數(shù)據(jù)的對(duì)比實(shí)驗(yàn)發(fā)現(xiàn):基于參數(shù)化點(diǎn)擴(kuò)散函數(shù)的盲解卷積模型、基于Non-local正則項(xiàng)的解卷積模型與基于稀疏表示的解卷積模型這三種思路都有著較為顯著的解卷積效果。
[Abstract]:Fluorescence microscopic imaging method is used as the ultraviolet irradiation source, irradiated cells were detected, the cell structure of the fluorescence and then observe the cell shape and other characteristics under the microscope. In biomedical research, through the fluorescence imaging technology, you can view the structure and biological activities in vivo cellular mechanism. But due to various factors the imaging system, the image resolution will be reduced, and the deconvolution is to improve resolution of image degradation. There are usually two deconvolution methods available: optical and optical method is calculated. The defocus before the light reaches the detector will block it to reduce distortion. Although 3D optical resolution image processing the more obvious, but the image contains serious anisotropy. In the calculation method of deconvolution, data is processed by a computer, to reduce the noise in the image And fuzzy, the work of this paper is to improve the image resolution by calculation method. Provides three deconvolution ideas in this paper. The traditional deconvolution methods are non blind deconvolution, applies only to the point spread function is known, we designed a deconvolution method based on parametric point spread function model, the the point spread function fitting for multiple Gauss superposition constructed form ER method for blind deconvolution, and then solved by alternate optimization method, we combine multiple regularization method TV and Hessian regular ER for deconvolution. Second kinds of ideas is the likelihood unchanged under the condition of Non-local is put forward and the regularization term. Applied to 2D space and 3D image sequence images, and uses the steepest descent method. The ideas of the three is to use the 3D fluorescence image below horizontal longitudinal resolution characteristics of the image along the longitudinal axis In order to make the interpolation, and horizontal resolution, and then using the sparse representation dictionary is applied to learning from the horizontal axis, and combining with the longitudinal axis of the Non-loal prior to improve the longitudinal resolution. This paper uses MATLAB R2012a and Visual Studio 2012 software for computer simulation, and the experimental results of each method and Richard-Lucy, the comparison of Richard-Lucy+TV and Wiener filtering method, and the deconvolution results in peak signal-to-noise ratio (PSNR), the structural similarity (SSIM) two performance indexes were compared and analyzed. Based on the simulation data and real data experiments found that parameters of the point spread function of the blind deconvolution model based on convolution model Non-local regularization based on sparse representation based deconvolution model of the three methods have obvious deconvolution effect.

【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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