發(fā)布時(shí)間：2018-03-14 12:01

  本文選題：圖像超分辨率　切入點(diǎn)：低秩矩陣稀疏分解　出處：《山東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在科技不斷取得創(chuàng)新和發(fā)展的大背景下,數(shù)字多媒體技術(shù)(Digital Multimedia Technology)也得到了推廣及應(yīng)用。其中,圖像視覺信號已成為數(shù)字化多媒體傳播信息的主要載體之一,大量的成像設(shè)備(如手機(jī)、數(shù)碼相機(jī)和iPad等)不斷被設(shè)計(jì)出來。獲取高質(zhì)量的圖像是人們不斷追求的目標(biāo)。然而在成像過程中,由于受外界各種不確定因素的干擾,人們最終獲得的圖像會失真。為了追求獲取高質(zhì)量的圖像,經(jīng)過研究者們的不斷研究與探索,圖像超分辨率(Image Super Resolution,ISR)重建理論不斷得到完善,一些實(shí)際的可用性成果已被研究出來。在壓縮感知(Compressed Sensing,CS)理論的發(fā)展影響下,衍生出一些有效的ISR重建算法。其中,基于信號稀疏表示(Sparse Representation)的ISR重建算法以其相對較好的重建結(jié)果受到研究者們的青睞;另一個(gè)與CS理論內(nèi)在相關(guān)的理論是圖像矩陣的低秩稀疏分解(Low Rank Sparse Decomposition,LRSD)理論,二者是數(shù)據(jù)的兩種不同表示方式�；趦煞N不同的信號表示理論基礎(chǔ),本文提出了一種新的圖像超分辨率重建算法:1.從圖像的結(jié)構(gòu)上分析,它的某些微結(jié)構(gòu)間具有相似性。本文首先利用某種特定的方式去分解圖像,然后通過圖像塊間的歐氏距離測量,找出結(jié)構(gòu)相似的圖像塊,并將圖像信號向量化表示,再將它們組成圖像矩陣。由于結(jié)構(gòu)相似的圖像塊信號間存在相關(guān)性,所以它們組成的矩陣是天然的低秩矩陣。以上述思想為基礎(chǔ),本文初步重建出一種基于低秩約束的圖像重建模型,初步重建出的初始高分辨率圖像可以保證與退化低分辨率觀測圖像基本結(jié)構(gòu)上的一致。2.第二步的工作目標(biāo)是恢復(fù)初始重建圖像中缺失的細(xì)節(jié)信息,即圖像中缺失的高頻成分�；谙∈璞硎镜膱D像重建算法是目前對圖像細(xì)節(jié)信息恢復(fù)重建表現(xiàn)較好的算法,本文借鑒它的思想來達(dá)到這一節(jié)的目的。特別與傳統(tǒng)字典訓(xùn)練方法不同的是,結(jié)合圖像低秩稀疏分解理論本節(jié)在樣本集的構(gòu)建階段提出創(chuàng)新,在提高字典訓(xùn)練效率的同時(shí)也改善了圖像的重建質(zhì)量。為了驗(yàn)證本文提出的重建算法的有效性,本文最后進(jìn)行了仿真實(shí)驗(yàn)。通過與該領(lǐng)域一些已有的表現(xiàn)較好的重建算法相比,在標(biāo)準(zhǔn)評價(jià)方法下的結(jié)果證實(shí)了本文提出的算法更具有優(yōu)越性,對圖像的細(xì)節(jié)部分恢復(fù)效果表現(xiàn)更好。
[Abstract]:In the background of continuous innovation and development of science and technology, digital Multimedia technology has also been popularized and applied. Among them, image visual signal has become one of the main carriers of digital multimedia communication information. A large number of imaging devices (such as mobile phones, digital cameras, iPad, etc.) are constantly being designed to obtain high-quality images. However, in the imaging process, due to various uncertainties outside the interference, In order to obtain high quality images, the theory of super-resolution image Super resolution (ISR) reconstruction has been improved through the research and exploration of researchers. Some practical usability results have been studied. Under the influence of compressed sensing theory, some effective ISR reconstruction algorithms are derived. The ISR reconstruction algorithm based on sparse representation of signals is favored by researchers for its relatively good results, and the theory of low rank sparse decomposition of image matrix and low Rank Sparse decompositionLRSD) is another theory that is intrinsically related to CS theory. They are two different representations of data. Based on two different signal representation theories, a new super-resolution image reconstruction algorithm:: 1 is proposed in this paper. Some of its microstructures are similar to each other. In this paper, the image is decomposed in a certain way, and then by measuring the Euclidean distance between the image blocks, the image blocks with similar structure are found, and the image signal is vectorized. Then they form the image matrix. Because of the correlation between the signals of similar image blocks, the matrix they form is a natural low rank matrix. In this paper, an image reconstruction model based on low rank constraint is proposed. The initial high-resolution image can be consistent with the basic structure of the degraded low-resolution observation image. The second step is to restore the missing detail information in the initial reconstructed image. The image reconstruction algorithm based on sparse representation is a good algorithm for image detail information restoration and reconstruction. This paper uses its ideas for reference to achieve the purpose of this section. In particular, different from the traditional dictionary training methods, combining with the theory of image low-rank sparse decomposition, this section proposes innovations in the construction of sample sets. The efficiency of dictionary training is improved and the image reconstruction quality is improved. In order to verify the effectiveness of the reconstruction algorithm proposed in this paper, simulation experiments are carried out at the end of this paper. The results under the standard evaluation method show that the proposed algorithm is more superior and performs better in the detail restoration of the image.
【學(xué)位授予單位】：山東師范大學(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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本文編號：1611116