本文選題：壓縮感知 + 過完備字典��； 參考：《浙江大學(xué)》2014年碩士論文

【摘要】：目前,國內(nèi)外各類光學(xué)遙感采樣成像系統(tǒng)均基于奈奎斯特—香農(nóng)采樣理論,它指出采樣率必須達到信號帶寬的兩倍以上才能精確地重構(gòu)信號。隨著遙感圖像空間分辨率的提高,要求光學(xué)系統(tǒng)的焦距更長、口徑更大,焦平面器件的采樣率更高,像元面積更小,這將大大增加光學(xué)系統(tǒng)、焦平面器件的設(shè)計和制造難度。而壓縮感知理論指出,只要信號是稀疏的或者在某一變換空間是稀疏的、可壓縮的,就可以遠低于奈奎斯特采樣定理所規(guī)定的采樣量得到信號的壓縮表示,并且仍能夠精確地重構(gòu)原始信號。因此,在遙感成像系統(tǒng)中采用壓縮感知理論進行圖像的壓縮采樣,可以在采樣的同時實現(xiàn)壓縮,獲取圖像迅速,節(jié)約工作時間；僅少量采樣值即可重構(gòu)原始高分辨圖像,極大地節(jié)約焦平面陣列器件,同時節(jié)省星上存儲空間；而針對不同類型的遙感圖像自適應(yīng)地選擇最優(yōu)稀疏表示方法,可以有效地提高遙感圖像重構(gòu)質(zhì)量,便于后期的信息提取工作；無論被觀測圖像類型如何,采用固定的觀測方式,均能獲得高質(zhì)量重構(gòu)圖像。 論文首先介紹了課題背景和意義,總結(jié)了壓縮感知理論框架中的常見圖像稀疏表示方法,圖像的隨機觀測手段及其觀測矩陣的構(gòu)造,介紹了壓縮感知中常見的優(yōu)化重構(gòu)算法,將壓縮感知理論在圖像復(fù)原和圖像融合領(lǐng)域提出了自己的創(chuàng)新和改進方法。 論文同時也介紹了幾種常用的圖像復(fù)原的方法,從主觀和客觀方面討論了圖像質(zhì)量評價的作用和意義。重點介紹了采用K-SVD方法進行過完備字典訓(xùn)練的方法。對不同類型遙感圖像的訓(xùn)練字典對于不同類型圖像的稀疏表示性能進行了深入分析。介紹了通過給定的訓(xùn)練字典對隨機觀測矩陣進行迭代優(yōu)化的聯(lián)合優(yōu)化方法,并且采用該方法進行實驗仿真,獲得了優(yōu)化后的觀測矩陣。同時,采用優(yōu)化后的觀測矩陣與訓(xùn)練字典對,測試了圖像的重構(gòu)效果。在此基礎(chǔ)上,深入分析并總結(jié)了重構(gòu)稀疏度與訓(xùn)練稀疏度倍率關(guān)系對于圖像重構(gòu)的影響,并提出了從觀測值中隨機選取對應(yīng)于原始圖像塊的少量觀測值序列進行小代價重構(gòu)從而預(yù)估出獲得良好重構(gòu)質(zhì)量的重構(gòu)稀疏度的方法,有利于快速且準確地找到最優(yōu)的重構(gòu)稀疏度從而獲得高重構(gòu)質(zhì)量。 論文最后提出了自適應(yīng)優(yōu)化稀疏表示的遙感圖像壓縮重構(gòu)框架,重點圍繞城市、山地和海港這三類遙感圖像進行了對應(yīng)類型圖像的字典訓(xùn)練,獲得這三類圖像的優(yōu)化稀疏表示字典,并分析對比了采用固定稀疏基DCT的稀疏表示精度。通過遙感圖像的壓縮感知粗復(fù)原初步判別遙感圖像類型,采用其對應(yīng)的優(yōu)化稀疏表示字典作為稀疏表示方法,再采用精確重構(gòu)算法對壓縮采樣值進行精確重構(gòu)。對重構(gòu)圖像采用主觀評價為輔,客觀評價中的PSNR和SSIM評價值進行分析。結(jié)論表明,采用本文提出的方法在提高遙感圖像壓縮重構(gòu)質(zhì)量上取得了較好的效果。
[Abstract]:At present, all kinds of optical remote sensing sampling and imaging systems at home and abroad are based on Nyquist Shannon sampling theory. It points out that the sampling rate must be more than twice of the signal bandwidth in order to accurately reconstruct the signal. With the improvement of spatial resolution of remote sensing image, the optical system is required to have longer focal length, larger aperture, higher sampling rate of focal plane device and smaller pixel area, which will greatly increase the difficulty of design and manufacture of optical system and focal plane device. Compression sensing theory states that as long as the signal is sparse or sparse and compressible in a transformation space, the compressed representation of the signal can be obtained far less than the sample amount specified by Nyquist sampling theorem. And still can accurately reconstruct the original signal. Therefore, compression sensing theory can be used to compress images in remote sensing imaging system, which can compress images at the same time, obtain images quickly and save working time. Only a small number of sampling values can reconstruct original high-resolution images. The focal plane array devices are greatly saved and the on-board storage space is saved, and the quality of remote sensing image reconstruction can be effectively improved by adaptively selecting the optimal sparse representation method for different types of remote sensing images. It is convenient for information extraction in the later period, and high quality reconstructed images can be obtained by using a fixed observation method, regardless of the type of the observed image. Firstly, the paper introduces the background and significance of the subject, summarizes the common image sparse representation methods in the theory framework of compressed perception, the random observation method of image and the construction of observation matrix, and introduces the common optimization reconstruction algorithms in compressed perception. The compression perception theory is proposed in the field of image restoration and image fusion. At the same time, several commonly used methods of image restoration are introduced, and the function and significance of image quality evaluation are discussed from subjective and objective aspects. The method of complete dictionary training using K-SVD method is introduced emphatically. The sparse representation performance of different types of remote sensing images is analyzed in detail by training dictionaries of different types of remote sensing images. A joint optimization method for iterative optimization of random observation matrix by a given training dictionary is introduced. The optimized observation matrix is obtained by using this method for experimental simulation. At the same time, the image reconstruction effect is tested by the optimized observation matrix and training dictionary. On this basis, the effect of the relationship between the sparse degree of reconstruction and the ratio of training sparsity on image reconstruction is analyzed and summarized. A method is proposed to estimate the reconstruction sparsity with good reconstruction quality by randomly selecting a small number of observation value sequences corresponding to the original image blocks from the observed values for small cost reconstruction. It is helpful to find the optimal reconstruction sparsity quickly and accurately and obtain high reconstruction quality. At the end of this paper, an adaptive and sparse image compression and reconstruction framework is proposed, which focuses on the dictionary training of the corresponding types of remote sensing images around the cities, mountains and seaports. The optimized sparse representation dictionaries of these three kinds of images are obtained, and the sparse representation accuracy using fixed sparse base DCT is analyzed and compared. The types of remote sensing images are preliminarily identified by the compressed perception rough restoration of remote sensing images. The corresponding optimized sparse representation dictionary is used as the sparse representation method, and the precise reconstruction algorithm is used to reconstruct the compressed sampling values accurately. The subjective evaluation is used as the assistant to reconstruct the image, and the PSNR and SSIM evaluation values in the objective evaluation are analyzed. The results show that the proposed method can improve the quality of remote sensing image compression and reconstruction.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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