本文關鍵詞： 圖像重建 稀疏表示 字典學習 K-SVD算法　出處：《山東師范大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著計算機以及互聯(lián)網(wǎng)技術的日新月異的發(fā)展,網(wǎng)絡上出現(xiàn)了大量的多媒體信息(如視頻、圖像、聲音等),這些復雜多樣的數(shù)據(jù)將人類帶入到了計算機網(wǎng)絡大數(shù)據(jù)時代。而這些信息數(shù)量龐大并且占用內(nèi)存空間多又不便于存儲和傳輸,如何從互聯(lián)網(wǎng)上將這些繁冗的信息進行有效的檢索、保存、挖掘其中蘊含的科技、商業(yè)和生活價值,成為如今需要人類急切處理的問題。人類是通過五官的感覺去獲取信息的,而人類接收、感知周圍環(huán)境事物的主要手段是視覺和語音信息。據(jù)最新研究統(tǒng)計,人類視覺所獲得的信息占人類所有獲取信息的接近百分之八十[1]。俗話說:眼睛是人類心靈的窗戶,人眼看到的信息容量大、范圍廣。對于信息的采樣及恢復,傳統(tǒng)的Nyquist采樣定理[2]給出了傳統(tǒng)的框架,并確定了能夠準確恢復原始信息的條件,即采樣的頻率應大于信號帶寬的兩倍以上。由于原理條件的限制及信息本身的特點,給信息的存儲、傳輸?shù)确矫鎺淼睦щy在迅速增加。近年來,稀疏表示(Sparse representation)理論的出現(xiàn)引起了研究人員和學者的特殊關注。稀疏表示原理避免了傳統(tǒng)的Nyquist采樣原理的限制,將信號投影到特定的變換域,并根據(jù)信號在該域內(nèi)的特有的稀疏特點及優(yōu)化方法,達到恢復出原有的信號的目的。近幾年來,機器學習、人工智能以及模式識別等技術引起人們的高度關注,本文在稀疏表示原理的基礎之上,融合機器學習、人工智能等相關領域的知識,給出了一種基于特征學習的圖像超分辨率重建算法。針對典型的特征提取算法提取的特征長度大,算法運行時所占用的空間多,導致算法運行時間長計算復雜度高的限制。在字典訓練過程的特征提取階段,本文通過提取圖像的中頻特征進行處理來減少運算時間,提高其運行的效率。并采用現(xiàn)今非常流行的效率較高的K-SVD方法進行字典的訓練,并在字典訓練之前采用PCA(Principal Component Analysis,PCA)[3]方法進行特征塊的降維,來進一步降低算法的復雜。最后在重建階段采用Batch-OMP算法進行稀疏稀疏的編碼計算,提高了算法的精確度和重建圖像的品質(zhì)。
[Abstract]:With the rapid development of computer and Internet technology, a lot of multimedia information (such as video, image, etc.) appears on the network. Sound and so on, these complex and diverse data brought mankind into the era of computer network big data. And the amount of this information and the amount of memory space is not easy to store and transfer. How to effectively retrieve, preserve and excavate the technological, commercial and life values of these redundant information from the Internet has become a problem that human beings urgently need to deal with today. Human beings obtain information through the sense of five senses. And the main means for human beings to receive and perceive things in their surroundings are visual and phonetic information. According to the latest research statistics, The information obtained by human vision accounts for nearly 80% of all information obtained by human beings. As the saying goes: the eyes are the windows of the human mind, the information that the human eyes see has a large capacity and a wide range. The traditional Nyquist sampling theorem [2] gives the traditional frame, and determines the conditions under which the original information can be recovered accurately, that is, the sampling frequency should be more than twice the bandwidth of the signal. The difficulties in storage and transmission of information are increasing rapidly. In recent years, the emergence of sparse representation theory has attracted special attention of researchers and scholars. The principle of sparse representation avoids the limitation of traditional Nyquist sampling principle. The signal is projected to a specific transform domain, and the original signal is restored according to the special sparse characteristic and optimization method of the signal in this domain. In recent years, machine learning, Artificial intelligence and pattern recognition have attracted great attention. Based on sparse representation principle, this paper combines the knowledge of machine learning, artificial intelligence and other related fields. An image super-resolution reconstruction algorithm based on feature learning is presented. In the process of dictionary training, the if feature of the image is extracted and processed to reduce the computation time. The K-SVD method, which is very popular nowadays, is used to train dictionaries, and the PCA(Principal Component Analysis (PCA3) method is used to reduce the dimension of feature blocks before the dictionary training. In order to reduce the complexity of the algorithm, Batch-OMP algorithm is used for sparse and sparse coding in the reconstruction stage, which improves the accuracy of the algorithm and the quality of the reconstructed image.
【學位授予單位】：山東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：1512594