發(fā)布時間：2018-06-05 03:00

  本文選題：稀疏表示 + 字典學習�。� 參考：《北京工業(yè)大學》2014年碩士論文

【摘要】：圖像稀疏表示理論的研究已成為近幾年來圖像處理領域的研究熱點。研究主要基于字典學習算法的設計、快速有效的稀疏表示算法以及該理論在圖像處理中的應用。將圖像信息轉化到稀疏域，很多方面可以大大簡化后續(xù)圖像的分析、處理過程，對圖像處理領域的研究具有重要的理論意義。 本文在中科院“一三五”規(guī)劃項目“空間大數據與數據密集型計算”的資助下，借助海量遙感數據在時間維高度冗余的特性進行海量相似高冗余數據的字典學習算法的研究，從而更加稀疏、高效地實現該類數據的稀疏表示，推動了圖像稀疏表示領域研究的發(fā)展。 首先，本文分析了傳統的字典學習算法不適應海量數據的原因，借鑒增量學習思想并結合K-SVD字典學習算法提出了一種可以分批訓練樣本的增量K-SVD字典學習算法。突破了傳統算法需要將樣本集中進行訓練的缺點。該算法將每幅圖像看做一個小樣本，對每次添加的樣本有選擇地訓練原子并添加到原子庫（字典）中。這樣，隨著樣本的不斷添加，字典中的原子特性越豐富，既能有效地表示當前新樣本又不影響對原始樣本的表示效果，從而實現對海量樣本的字典學習。 其次，提出了一種基于信息熵的字典原子初值遴選方法。字典學習過程中首先字典中的原子需要設置初值，本文通過計算每列稀疏系數的熵值判斷稀疏系數分布的差異情況，，熵值較大的稀疏系數列對應的信號即為不易被稀疏表示的結構，將該類信號設為原子的初始值，使得訓練出的原子更加符合當前樣本結構，且豐富了原子庫信息。 然后，對字典訓練過程進行去相干處理。本文研究了多種字典去相干方法，提出一種動態(tài)去字典相干性的模型。該模型在字典學習過程中引入相干參數作為判決條件。對于動態(tài)添加的原子組合，判斷其對字典相干性的影響。對使得字典相干性高于閾值的原子組合，算法首先利用迭代投影方法確保字典滿足相干參數，然后對該原子組合在稀疏逼近殘差的目標函數下進行迭代旋轉并且不影響字典的相干性。保證字典相干性的同時，使得原子逼近訓練樣本。 最后，本文選取大量的時間序列上的Landsat遙感衛(wèi)星數據做實驗樣本。將本文算法與另外兩種可以訓練動態(tài)數據集的字典學習算法做比較。實驗結果表明，本文算法能夠更加有效、更加稀疏地表示出原始數據。
[Abstract]:Image sparse representation theory has become a hotspot in the field of image processing in recent years. The research is mainly based on the design of dictionary learning algorithm, the fast and effective sparse representation algorithm and the application of this theory in image processing. To transform image information into sparse domain, many aspects can greatly simplify the analysis and processing process of subsequent images, which has important theoretical significance for the research of image processing field. In this paper, with the aid of "Spatial big data and Data-intensive Computing", a planning project of the 13th Five-Year Plan of the Chinese Academy of Sciences, a dictionary learning algorithm for massive similar and highly redundant data is studied with the help of the characteristics of massive remote sensing data with high redundancy in time dimension. Therefore, the sparse representation of this kind of data is realized more sparsely and efficiently, which promotes the development of image sparse representation field. Firstly, this paper analyzes the reason why the traditional dictionary learning algorithm can not adapt to the massive data, and proposes an incremental K-SVD dictionary learning algorithm based on the incremental learning idea and the K-SVD dictionary learning algorithm. It breaks through the shortcoming of the traditional algorithm which needs to train the sample set. The algorithm treats each image as a small sample and selectively trains atoms and adds them to the atomic library (dictionary). In this way, with the continuous addition of samples, the more atomic properties in the dictionary, can effectively represent the current new samples without affecting the performance of the original samples, so as to achieve the dictionary learning of massive samples. Secondly, a dictionary atomic initial value selection method based on information entropy is proposed. In the process of dictionary learning, first of all, the atoms in the dictionary need to set initial values. In this paper, the difference in the distribution of sparse coefficients is determined by calculating the entropy value of each column of sparse coefficients. The signal corresponding to the sparse coefficient column with large entropy value is a structure that is not easily represented by sparse representation. By setting this kind of signal as the initial value of the atom, the trained atom conforms to the current sample structure and enriches the atomic library information. Then, the dictionary training process is de-coherent. In this paper, we study a variety of dictionary de-coherence methods, and propose a dynamic de-coherence model. In this model, coherent parameters are introduced as decision conditions in dictionary learning process. For dynamically added atomic combinations, the effect on dictionary coherence is judged. For the combination of atoms whose coherence is higher than the threshold value, the iterative projection method is used to ensure that the dictionary satisfies the coherent parameters. Then the atomic combination is rotated iteratively under the objective function of sparse approximation residuals without affecting the coherence of the dictionary. At the same time, the atoms approach the training sample while ensuring the consistency of the dictionary. Finally, a large number of Landsat remote sensing satellite data on time series are selected as experimental samples. This algorithm is compared with the other two dictionary learning algorithms which can train dynamic data sets. Experimental results show that the proposed algorithm is more efficient and more sparse to represent the original data.
【學位授予單位】：北京工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751

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本文編號：1980149