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  本文選題：壓縮感知　切入點：圖像重構(gòu)　出處：《南京理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：壓縮感知理論是一種新穎的采樣理論,和傳統(tǒng)的采樣理論比較,其優(yōu)勢主要有兩個。一個是信號的壓縮和采樣是同時進行的,另一個是在信號采樣率較低的情況下重構(gòu)原始信號。作為壓縮感知理論的核心內(nèi)容之一,重構(gòu)算法的好壞影響著壓縮感知理論在實際應(yīng)用的情況。近些年信號的重構(gòu)問題被應(yīng)用于圖像復(fù)原、語言處理、地質(zhì)勘探等方面,如何設(shè)計出高質(zhì)量的壓縮感知重構(gòu)算法來精確重構(gòu)出原始信號,是相關(guān)領(lǐng)域研究人員的研究重點。在這背景下,本文以圖像壓縮感知重構(gòu)算法為研究對象,尋求高質(zhì)量的壓縮感知重構(gòu)算法。本文主要研究以下三個方面的內(nèi)容:(1)對壓縮采樣匹配追蹤(CoSaMP)算法進行了改進。在匹配追蹤類算法中CoSaMP算法對信號的重構(gòu)效果比較好,考慮該算法中的內(nèi)積運算不能最大限度地體現(xiàn)出殘差向量和觀測矩陣中原子的關(guān)聯(lián)度,然而相關(guān)系數(shù)可以更好地表示向量間的關(guān)聯(lián)程度。為此本文提出了一種優(yōu)化的CoSaMP圖像壓縮感知重構(gòu)算法,并在一維模擬信號和圖像信號上進行實驗,通過實驗證明該算法的重構(gòu)精度更高。(2)對基于GPU并行加速的圖像壓縮感知重構(gòu)算法進行了研究。非局部低秩正則化的壓縮感知(NLR-CS)算法對圖像有較好的重構(gòu)效果,但NLR-CS算法存在運行時間較長的缺點,因此可引入目前流行的GPU并行加速技術(shù),提出一種基于GPU的并行NLR-CS算法。在熟悉NLR-CS算法流程的基礎(chǔ)上,對串行執(zhí)行方式進行熱點分析,找出算法的性能瓶頸,同時分析算法的可并行性。再結(jié)合并行圖像處理技術(shù),根據(jù)GPU的硬件特性,采用CUDA編程模型對NLR-CS算法進行并行化設(shè)計和實現(xiàn),最后通過實驗證明該算法能夠在不影響圖像重構(gòu)質(zhì)量的情況下取得良好的加速效果。(3)對當(dāng)前較好的圖像壓縮感知(NLR-CS)算法進行了改進。該算法利用低秩正則化對圖像進行重構(gòu),但該算法僅利用圖像的非局部相似性特征,未考慮圖像的局部結(jié)構(gòu)特征,重構(gòu)出的圖像不能較好地保留圖像的紋理信息。為此本文提出一種基于低秩和全變差正則化的圖像壓縮感知重構(gòu)算法,算法結(jié)合了圖像的非局部相似性、局部梯度稀疏信息以及傳統(tǒng)的壓縮感知理論,構(gòu)造出新的重構(gòu)模型。最后采用交替方向乘子法實現(xiàn)圖像的重構(gòu),在圖像信號進行實驗,通過實驗證明該算法重構(gòu)的圖像效果更好。
[Abstract]:Compression sensing theory is a new sampling theory. Compared with the traditional sampling theory, it has two main advantages. One is that the compression and sampling of signals are carried out simultaneously. The other is to reconstruct the original signal when the sampling rate is low. In recent years, the problem of signal reconstruction has been applied to image restoration, language processing, geological exploration and so on. How to design a high quality compression perception reconstruction algorithm to accurately reconstruct the original signal is the research focus of the researchers in related fields. Under this background, this paper takes the image compression perception reconstruction algorithm as the research object. In this paper, we mainly study the following three aspects: we improve the compression sampling matching tracking CoSMP algorithm. In the matching tracking algorithm, the CoSaMP algorithm has a good effect on signal reconstruction. Considering the inner product operation in this algorithm, the correlation degree between the residual vector and the atoms in the observation matrix can not be maximized. However, the correlation coefficient can better represent the correlation between vectors. In this paper, an optimized CoSaMP image compression perceptual reconstruction algorithm is proposed, and experiments are carried out on one-dimensional analog signals and image signals. It is proved by experiments that the algorithm has higher reconstruction accuracy. (2) the image compression perceptual reconstruction algorithm based on GPU parallel acceleration is studied. The non-local low rank regularized compression sensing algorithm has good effect on image reconstruction. But the NLR-CS algorithm has the disadvantage of long running time, so we can introduce the popular GPU parallel acceleration technology, and propose a parallel NLR-CS algorithm based on GPU. Based on the familiar with the NLR-CS algorithm flow, the serial execution mode is analyzed. Find out the performance bottleneck of the algorithm, at the same time analyze the parallelism of the algorithm, then combine the parallel image processing technology, according to the hardware characteristic of GPU, adopt the CUDA programming model to design and implement the NLR-CS algorithm parallelism. Finally, experiments show that the algorithm can achieve a good acceleration effect without affecting the quality of image reconstruction. It improves the current better image compression perception (NLR-CS) algorithm. The algorithm uses low-rank regularization to reconstruct the image. However, the algorithm only uses the non-local similarity feature of the image, and does not consider the local structure feature of the image. The reconstructed image can not preserve the texture information of the image. In this paper, an image compression perceptual reconstruction algorithm based on low rank and total variation regularization is proposed, which combines the non-local similarity of the image. Based on the local gradient sparse information and the traditional theory of compression perception, a new reconstruction model is constructed. Finally, the alternating direction multiplier method is used to reconstruct the image, and the experiment is carried out on the image signal. Experiments show that the algorithm has better image effect.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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