基于過完備字典的非凸壓縮感知理論與方法研究
發(fā)布時間：2018-07-26 18:29
【摘要】：壓縮感知是一種全新的信號獲取和處理框架,其理論和技術的發(fā)展將對數(shù)字信號的獲取方式,分析技術和處理方法等研究領域及相關應用領域產生深遠的影響。目前,壓縮感知正從理論研究向實際的信號應用領域發(fā)展:處理的數(shù)據(jù)對象從具有簡單的理想稀疏性信號轉向廣泛的具有復雜低維結構的實際信號;信號的稀疏表示從基于正交基和框架發(fā)展為基于結構化的冗余字典;研究重點從理論研究發(fā)展為對應用中實際信號的重構和處理。其中,根據(jù)應用需求建立結構化重構模型以及高效的重構算法是壓縮感知從理論走向實踐中最重要的環(huán)節(jié),也是壓縮感知應用研究的熱點。本論文工作中,建立了分塊策略下的基于過完備字典的圖像非凸壓縮感知框架,其中,對圖像進行分塊壓縮觀測,即對圖像的每個大小相等的圖像塊使用相同的隨機觀測方式;構造了Ridgelet過完備字典,獲得對任意圖像塊的稀疏表示;挖掘和利用圖像塊在Ridgelet過完備字典中的稀疏性和結構稀疏先驗,設計了從圖像塊的壓縮觀測中獲得圖像準確估計的重構模型。在此框架下,針對壓縮感知重構的本源問題,即0l范數(shù)約束的非凸優(yōu)化問題,我們提出了基于自然計算優(yōu)化算法和協(xié)同優(yōu)化的重構思路,建立了能夠有效求解包含了非凸稀疏先驗以及多種結構先驗約束的圖像重構方法。論文的主要工作包括:(1)為了獲得全局尋優(yōu)意義下的非凸壓縮感知重構,提出了基于自然計算優(yōu)化算法的兩階段重構框架。在該框架的第一階段,設計了一種遺傳算法來獲得一類圖像塊在方向上的最優(yōu)原子組合;第二階段在第一階段結果的基礎上,設計了一種克隆選擇算法來搜索自適應于每個圖像塊的子字典,并獲得每個圖像塊在尺度和位移等參數(shù)上的更優(yōu)原子組合。該框架采用全局尋優(yōu)的進化搜索策略,通過靈活多樣的進化策略設計來實現(xiàn)零范數(shù)和圖像結構先驗約束下的圖像分塊壓縮感知重構。該工作是自然計算優(yōu)化方法在非凸壓縮感知重構中的成功應用嘗試,能夠獲得對圖像較好的重構估計。(2)考慮到基于進化搜索策略的重構方法存在重構速度較慢的問題,提出了基于過完備字典的協(xié)同壓縮感知重構,其主要思想是用匹配追蹤方法的局部搜索和交迭優(yōu)化策略代替進化搜索中的全局搜索策略。該方法利用了圖像的自相似特性,設計了兩種協(xié)同重構方式,用于在局部和非局部相似的圖像塊間進行重構信息的傳遞和交換。第一種協(xié)同方式利用一組相似圖像塊的觀測向量來重構單個圖像塊,第二種協(xié)同方式則利用一組圖像塊的估計值來獲得對單個圖像塊的更優(yōu)估計。實驗結果表明,所提出的方法可以有效減少采用基于進化搜索策略的重構方法的運行時間,在性能上超過了經典匹配追蹤算法。(3)為了獲得對圖像塊局部結構的更準確估計,并提升已有協(xié)同重構方法,提出了一種幾何結構指導的協(xié)同重構方法。該方法根據(jù)過完備字典中原子結構與圖像塊結構的匹配關系,對圖像塊在字典中的稀疏表示系數(shù)施加塊稀疏結構約束,并將這些約束與協(xié)同重構機制結合,分別設計了針對光滑,單方向和隨機結構圖像塊的協(xié)同重構模式和重構策略。與已有的協(xié)同重構方法相比,結合了幾何結構先驗的協(xié)同重構方法能夠有效改善圖像局部結構估計,并在重構精度和速度上都有所提升。(4)為了結合和利用圖像塊基于過完備字典的方向結構先驗來獲得對圖像及其局部結構的準確重構,提出了基于方向指導的字典及進化搜索的重構策略。其中設計和提出了一種利用Ridgelet過完備字典根據(jù)圖像塊的壓縮觀測判定圖像塊結構類型的解析方法,將圖像塊判定為光滑,單方向和多方向塊中的一種,并對單方向和多方向塊的方向結構進行估計。根據(jù)對圖像塊的結構估計,我們?yōu)楣饣蛦畏较驁D像塊構造了稀疏子字典,并設計了方向指導的進化搜索重構策略。該重構策略中,對光滑圖像塊采用單階段的進化重構策略;對單方向和多方向圖像塊首先基于方向指導的結構稀疏模型進行重構,再采用進化搜索策略進行再次優(yōu)化估計。與已有的兩階段進化重構策略相比,本重構策略能夠獲得更準確的方向結構估計,以及更高的重構速度。通過本工作,展示了基于進化搜索的優(yōu)化方法在具有非凸稀疏約束及其他結構先驗共同約束的優(yōu)化問題中的應用前景。
[Abstract]:Compressed sensing is a new framework for signal acquisition and processing. The development of its theory and technology will have a profound impact on the research fields of digital signal acquisition, analysis technology and processing methods and related applications. At present, compression perception is developing from theoretical research to real signal application field: data object processing. From simple ideal sparsity signals to a wide range of practical signals with complex and low dimensional structures; sparse representations of signals are developed from based on orthogonal bases and frameworks to structured redundant dictionaries; the focus of research is developed from theoretical research to the reconstruction and processing of practical signals in applications. The most important part of compression perception from theory to practice is the most important part of compression perception from theory to practice, and also a hot spot in compressed sensing application research. In this paper, an image non convex compression frame based on overcomplete dictionary based on partitioned strategy is established, in which the image is divided into block compression observation, that is, to the image Each image block with equal size uses the same random observation method; constructs the Ridgelet overcomplete dictionary to obtain the sparse representation of any image block, and uses the sparsity and the sparse priori in the Ridgelet overcomplete dictionary to excavate and make use of the image block in the overcomplete dictionary of the image. In this framework, in view of the source problem of compressed sensing reconstruction, that is, the non convex optimization problem of 0l norm constraint, we propose a reconstruction idea based on the natural computing optimization algorithm and the cooperative optimization, and establish an image reconstruction method which can effectively solve the non convex sparse prior and a variety of structure prior constraints. The work includes: (1) in order to obtain the non convex compression perception reconstruction under the global optimization, a two phase reconstruction framework based on the natural computing optimization algorithm is proposed. In the first stage of the framework, a genetic algorithm is designed to obtain the optimal combination of a class of image blocks in the direction; the second stage is based on the first stage result. A clonal selection algorithm is designed to search the sub dictionaries adaptive to each image block and obtain better atomic combinations of each image block on the parameters of scale and displacement. The framework uses a global optimization evolutionary search strategy to achieve zero norm and image structure prior constraints by a flexible and diverse evolutionary strategy design. This work is a successful application of natural computing optimization method in non convex compression sensing reconstruction, which can obtain better reconstruction estimation for images. (2) considering the problem of slow reconstruction in the reconstruction method based on evolutionary search strategy, a cooperative compression perception based on overcomplete dictionary is proposed. The main idea is to replace the global search strategy in the evolutionary search with the local search and overlapping optimization strategy of matching pursuit method. The method uses the self similarity of the image, and designs two cooperative reconfiguration methods for the transfer and exchange of the reconstruction information between the local and non local similar image blocks. In the same way, an observation vector of a group of similar image blocks is used to reconstruct a single image block. The second cooperative methods use an estimated value of a block of image blocks to obtain a better estimate of a single image block. The experimental results show that the proposed method can effectively reduce the running time of the reconfiguration method based on the evolutionary search strategy. 3. (3) in order to obtain more accurate estimation of the local structure of the image block, and to improve the existing cooperative reconfiguration method, a collaborative reconstruction method guided by geometric structure is proposed. The representation coefficient applies the block sparse structure constraint, and combines these constraints with the cooperative reconfiguration mechanism, designs the cooperative reconstruction mode and reconfiguration strategy for smooth, single direction and random structure image blocks respectively. Compared with the existing cooperative reconstruction method, the cooperative reconstruction method combining the geometric structure first test can effectively improve the image part. The structure estimation and the reconstruction precision and speed have been improved. (4) in order to combine and utilize the image block based on the direction structure prior to the overcomplete dictionary, the accurate reconstruction of the image and its local structure is obtained. A dictionary based on direction guidance and the reconstruction strategy of evolutionary search are proposed, in which a kind of use of Ridgelet is designed and proposed. An overcomplete dictionary determines the structure type of the block according to the compression observation of the image block. The image block is determined as one of the smooth, single and multi direction blocks, and the direction structure of the single direction and multi direction block is estimated. According to the structure estimation of the image block, we construct the sparsity for the smooth and single direction image blocks. An evolutionary search reconfiguration strategy directed by direction is designed. In this reconfiguration strategy, a single stage evolutionary reconfiguration strategy is adopted for smooth image blocks; a single directional and multi direction image block is restructured in the first direction based structural sparse model, and then the evolutionary search strategy is used for the reoptimization estimation. The two phase of the reconfiguration is made with the existing evolutionary search strategy. Compared with the evolutionary reconstruction strategy, this reconfiguration strategy can obtain more accurate direction structure estimation and higher reconstruction speed. Through this work, the optimization method based on evolutionary search is shown to be applied in the optimization problem with non convex sparse constraints and other structural priori constraints.
【學位授予單位】：西安電子科技大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41
本文編號：2146917
[Abstract]:Compressed sensing is a new framework for signal acquisition and processing. The development of its theory and technology will have a profound impact on the research fields of digital signal acquisition, analysis technology and processing methods and related applications. At present, compression perception is developing from theoretical research to real signal application field: data object processing. From simple ideal sparsity signals to a wide range of practical signals with complex and low dimensional structures; sparse representations of signals are developed from based on orthogonal bases and frameworks to structured redundant dictionaries; the focus of research is developed from theoretical research to the reconstruction and processing of practical signals in applications. The most important part of compression perception from theory to practice is the most important part of compression perception from theory to practice, and also a hot spot in compressed sensing application research. In this paper, an image non convex compression frame based on overcomplete dictionary based on partitioned strategy is established, in which the image is divided into block compression observation, that is, to the image Each image block with equal size uses the same random observation method; constructs the Ridgelet overcomplete dictionary to obtain the sparse representation of any image block, and uses the sparsity and the sparse priori in the Ridgelet overcomplete dictionary to excavate and make use of the image block in the overcomplete dictionary of the image. In this framework, in view of the source problem of compressed sensing reconstruction, that is, the non convex optimization problem of 0l norm constraint, we propose a reconstruction idea based on the natural computing optimization algorithm and the cooperative optimization, and establish an image reconstruction method which can effectively solve the non convex sparse prior and a variety of structure prior constraints. The work includes: (1) in order to obtain the non convex compression perception reconstruction under the global optimization, a two phase reconstruction framework based on the natural computing optimization algorithm is proposed. In the first stage of the framework, a genetic algorithm is designed to obtain the optimal combination of a class of image blocks in the direction; the second stage is based on the first stage result. A clonal selection algorithm is designed to search the sub dictionaries adaptive to each image block and obtain better atomic combinations of each image block on the parameters of scale and displacement. The framework uses a global optimization evolutionary search strategy to achieve zero norm and image structure prior constraints by a flexible and diverse evolutionary strategy design. This work is a successful application of natural computing optimization method in non convex compression sensing reconstruction, which can obtain better reconstruction estimation for images. (2) considering the problem of slow reconstruction in the reconstruction method based on evolutionary search strategy, a cooperative compression perception based on overcomplete dictionary is proposed. The main idea is to replace the global search strategy in the evolutionary search with the local search and overlapping optimization strategy of matching pursuit method. The method uses the self similarity of the image, and designs two cooperative reconfiguration methods for the transfer and exchange of the reconstruction information between the local and non local similar image blocks. In the same way, an observation vector of a group of similar image blocks is used to reconstruct a single image block. The second cooperative methods use an estimated value of a block of image blocks to obtain a better estimate of a single image block. The experimental results show that the proposed method can effectively reduce the running time of the reconfiguration method based on the evolutionary search strategy. 3. (3) in order to obtain more accurate estimation of the local structure of the image block, and to improve the existing cooperative reconfiguration method, a collaborative reconstruction method guided by geometric structure is proposed. The representation coefficient applies the block sparse structure constraint, and combines these constraints with the cooperative reconfiguration mechanism, designs the cooperative reconstruction mode and reconfiguration strategy for smooth, single direction and random structure image blocks respectively. Compared with the existing cooperative reconstruction method, the cooperative reconstruction method combining the geometric structure first test can effectively improve the image part. The structure estimation and the reconstruction precision and speed have been improved. (4) in order to combine and utilize the image block based on the direction structure prior to the overcomplete dictionary, the accurate reconstruction of the image and its local structure is obtained. A dictionary based on direction guidance and the reconstruction strategy of evolutionary search are proposed, in which a kind of use of Ridgelet is designed and proposed. An overcomplete dictionary determines the structure type of the block according to the compression observation of the image block. The image block is determined as one of the smooth, single and multi direction blocks, and the direction structure of the single direction and multi direction block is estimated. According to the structure estimation of the image block, we construct the sparsity for the smooth and single direction image blocks. An evolutionary search reconfiguration strategy directed by direction is designed. In this reconfiguration strategy, a single stage evolutionary reconfiguration strategy is adopted for smooth image blocks; a single directional and multi direction image block is restructured in the first direction based structural sparse model, and then the evolutionary search strategy is used for the reoptimization estimation. The two phase of the reconfiguration is made with the existing evolutionary search strategy. Compared with the evolutionary reconstruction strategy, this reconfiguration strategy can obtain more accurate direction structure estimation and higher reconstruction speed. Through this work, the optimization method based on evolutionary search is shown to be applied in the optimization problem with non convex sparse constraints and other structural priori constraints.
【學位授予單位】：西安電子科技大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41
【相似文獻】
相關期刊論文 前10條
1 顧勇;張燦果;龔志廣;;基于圖像塊分割融合算法在醫(yī)學圖像中的應用[J];河北建筑工程學院學報;2007年02期
2 李天偉;黃謙;郭模燦;何四華;;圖像塊混沌特征在海面運動目標檢測中的應用[J];中國造船;2011年02期
3 李軍;部分圖像塊的顯示及特技制作技巧[J];電腦編程技巧與維護;1997年04期
4 李生金;蒲寶明;賀寶岳;王維維;;基于圖像塊的滯留物/移取物的檢測方法[J];小型微型計算機系統(tǒng);2014年01期
5 趙德斌;陳耀強;高文;;基于圖像塊方向的自適應無失真編碼[J];模式識別與人工智能;1998年01期
6 陳琦,李華,朱光喜;一種新的應用于屏幕共享的圖像塊識別算法[J];電訊技術;2000年06期
7 劉尚翼;霍永津;羅欣榮;白仲亮;魏林鋒;項世軍;;基于圖像塊相關性分類的加密域可逆數(shù)據(jù)隱藏[J];武漢大學學報(理學版);2013年05期
8 陳奮,閆冬梅,趙忠明;一種快速圖像塊填充算法及其在遙感影像處理中的應用[J];計算機應用;2005年10期
9 馬文龍,余寧梅,銀磊,高勇;圖像塊動態(tài)劃分矢量量化[J];計算機輔助設計與圖形學學報;2005年02期
10 李維釗,王廣偉;圖像塊平坦測度與系數(shù)掃描方式選擇[J];山東電子;2000年04期
相關會議論文 前2條
1 李趙紅;侯建軍;宋偉;;基于圖像塊等級模型的多重認證水印算法[A];第八屆全國信息隱藏與多媒體安全學術大會湖南省計算機學會第十一屆學術年會論文集[C];2009年
2 鐘凡;莫銘臻;秦學英;彭群生;;基于WSSD的不規(guī)則圖像塊快速匹配[A];中國計算機圖形學進展2008--第七屆中國計算機圖形學大會論文集[C];2008年
相關博士學位論文 前5條
1 霍雷剛;圖像處理中的塊先驗理論及應用研究[D];西安電子科技大學;2015年
2 欽夏孟;稠密圖像塊匹配方法及其應用[D];北京理工大學;2015年
3 林樂平;基于過完備字典的非凸壓縮感知理論與方法研究[D];西安電子科技大學;2016年
4 向濤;復雜場景下目標檢測算法研究[D];電子科技大學;2016年
5 宋偉;幾類數(shù)字圖像水印算法的研究[D];北京交通大學;2010年
相關碩士學位論文 前10條
1 王榮麗;基于半監(jiān)督學習的目標跟蹤方法研究[D];浙江師范大學;2015年
2 祝漢城;數(shù)字圖像的客觀質量評價方法研究[D];中國礦業(yè)大學;2015年
3 陸杰;使用自組織增量神經網(wǎng)絡實現(xiàn)單層非監(jiān)督特征學習[D];南京大學;2015年
4 熊耀先;基于圖像塊統(tǒng)計特性的EPLL遙感圖像復原方法[D];國防科學技術大學;2014年
5 張書揚;基于冗余字典的圖像壓縮感知技術研究[D];吉林大學;2016年
6 楊存強;基于圖像塊多級分類和稀疏表示的超分辨率重建算法研究[D];天津工業(yè)大學;2016年
7 魏譜躚;基于圖像塊特征的焊縫識別算法研究[D];西安科技大學;2014年
8 杭仁龍;基于主動學習的遙感圖像分類研究[D];南京信息工程大學;2014年
9 劉尚翼;加密域可逆信息隱藏研究[D];暨南大學;2014年
10 張曉旭;圖像內容可恢復的變容量脆弱水印算法研究[D];西南交通大學;2014年
，本文編號：2146917
本文鏈接：http://www.lk138.cn/shoufeilunwen/xxkjbs/2146917.html
最近更新
教材專著
