本文選題：去運(yùn)動(dòng)模糊　切入點(diǎn)：多正則化約束　出處：《工程科學(xué)與技術(shù)》2017年03期 　論文類型：期刊論文

【摘要】：針對(duì)圖像去運(yùn)動(dòng)模糊問(wèn)題的病態(tài)性,已有的方法通常引入對(duì)圖像的正則化約束從而縮小解空間范圍使其良態(tài)化,但單一的正則化約束并不能很好地估計(jì)點(diǎn)擴(kuò)散函數(shù)和復(fù)原原始圖像�；诖�,本文提出一種基于多正則化約束的圖像去運(yùn)動(dòng)模糊方法。首先,根據(jù)圖像梯度符合重尾分布的特性,采用歸一化的超拉普拉斯先驗(yàn)項(xiàng)作為對(duì)圖像先驗(yàn)約束的正則項(xiàng)。其次,分析描述圖像運(yùn)動(dòng)模糊的點(diǎn)擴(kuò)散函數(shù)的內(nèi)在特性包括稀疏性和連續(xù)光滑性;同時(shí),采用點(diǎn)擴(kuò)散函數(shù)自身的L1范數(shù)保證其稀疏性并作為其中一項(xiàng)點(diǎn)擴(kuò)散函數(shù)先驗(yàn)約束的正則項(xiàng),采用Tikhonov正則化約束保證其連續(xù)平滑性并作為另一項(xiàng)點(diǎn)擴(kuò)散函數(shù)先驗(yàn)約束的正則項(xiàng),避免估計(jì)的點(diǎn)擴(kuò)散函數(shù)中存在孤立的點(diǎn)。由于所建立的正則項(xiàng)雖然不可微但其是非嚴(yán)格凸函數(shù),故引入輔助變量采用分裂法和交替求解法對(duì)所建能量方程進(jìn)行求解,并利用小波軟閾值公式求解輔助變量。本文方法對(duì)合成的運(yùn)動(dòng)模糊圖像和實(shí)際相機(jī)抖動(dòng)造成的自然模糊圖像均進(jìn)行實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果驗(yàn)證了該模型和求解算法的有效性和快速性。實(shí)驗(yàn)結(jié)果表明,本文方法提高了點(diǎn)擴(kuò)散函數(shù)估計(jì)準(zhǔn)確度,同時(shí)提高了復(fù)原圖像質(zhì)量,具有較好的復(fù)原效果。
[Abstract]:In view of the ill-condition of image demotion blur problem, the existing methods usually introduce regularization constraints on images to narrow the solution space and make them better. However, a single regularization constraint can not estimate the point diffusion function and restore the original image very well. Based on this, this paper proposes an image de-motion blur method based on multiple regularization constraints. According to the characteristics of image gradient matching heavy-tailed distribution, the normalized super-Laplace priori term is used as the regular term for image priori constraint. The inherent properties of point diffusion function describing image motion blur include sparsity and continuous smoothness, meanwhile, the L _ 1 norm of point diffusion function is used to guarantee its sparsity and to be a regular term which is a priori constraint of point diffusion function. The Tikhonov regularization constraint is used to guarantee its continuous smoothness and to be a priori constraint of another point diffusion function. In order to avoid the existence of isolated points in the estimated point diffusion function, the established canonical terms are not differentiable but they are not strictly convex functions, so the auxiliary variables are introduced to solve the energy equation by splitting method and alternating solution method. Using the wavelet soft threshold formula to solve the auxiliary variables, both the synthetic motion blur image and the natural blur image caused by the camera jitter are tested in this paper. The experimental results verify the validity and rapidity of the model and the algorithm. The experimental results show that the proposed method improves the accuracy of point diffusion function estimation and the quality of restored images.
【作者單位】： 成都信息工程大學(xué)計(jì)算機(jī)學(xué)院;成都信息工程大學(xué)圖形圖像與空間信息協(xié)同創(chuàng)新中心;
【基金】：國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃資助項(xiàng)目(2014CB360506) 四川省科技支撐計(jì)劃資助項(xiàng)目(2015RZ0008) 四川省教育廳重點(diǎn)項(xiàng)目資助(15ZA0186) 國(guó)家自科基金青年基金資助項(xiàng)目(61303126)
【分類號(hào)】：TP391.41

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本文編號(hào)：1584222