本文關(guān)鍵詞： 秘密共享 圖像分存 壓縮感知 稀疏變換 信號(hào)重構(gòu)　出處：《內(nèi)蒙古大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：傳統(tǒng)秘密圖像分存技術(shù)需要對(duì)圖像的所有數(shù)據(jù)進(jìn)行處理,由于圖像數(shù)據(jù)量大,所以算法執(zhí)行時(shí)間比較長(zhǎng),而且分存算法產(chǎn)生的數(shù)據(jù)總量與原始圖像數(shù)據(jù)量相比擴(kuò)張明顯,會(huì)對(duì)網(wǎng)絡(luò)傳輸和存儲(chǔ)造成較大負(fù)擔(dān)。當(dāng)圖像信號(hào)具有稀疏性,或者在某一種稀疏變換基的表示下絕大部分系數(shù)為零或近似為零時(shí),壓縮感知技術(shù)通過(guò)構(gòu)造一個(gè)與稀疏基不相關(guān)的適合的測(cè)量矩陣對(duì)原始圖像進(jìn)行感知測(cè)量。測(cè)量得到的數(shù)據(jù)包含了原始圖像的絕大部分有用信息,在確保能夠精確重構(gòu)圖像的同時(shí)將原圖像從高維壓縮到低維,極大地減少了需要處理的數(shù)據(jù)量,能很好地解決傳統(tǒng)方法中由于數(shù)據(jù)量大而導(dǎo)致的諸多問(wèn)題。在信號(hào)重構(gòu)端,通過(guò)一定的重構(gòu)算法可以獲得原始圖像信息一個(gè)精確的或者高度近似的逼近。本文將傳統(tǒng)秘密圖像分存與壓縮感知技術(shù)相結(jié)合,實(shí)現(xiàn)了一個(gè)基于壓縮感知技術(shù)的秘密圖像分存方案。為了進(jìn)一步提高方案的性能,又從信號(hào)的稀疏表示、信號(hào)的感知測(cè)量和信號(hào)重構(gòu)三個(gè)方面對(duì)方案進(jìn)行了優(yōu)化。通過(guò)實(shí)驗(yàn)我們發(fā)現(xiàn)本文方案能明顯降低需要處理的數(shù)據(jù)量,有效減少算法執(zhí)行時(shí)間,并且重構(gòu)圖像可以達(dá)到一個(gè)較理想的視覺(jué)效果。實(shí)驗(yàn)結(jié)果表明:與經(jīng)典的Thien-Lin方案相比,本文實(shí)現(xiàn)的初始方案能平均減少48.66%的圖像分存時(shí)間和29.32%的圖像還原時(shí)間。優(yōu)化方案可以平均減少58.77%的分存時(shí)間和58.16%的還原時(shí)間,而且優(yōu)化方案的重構(gòu)圖像精度比初始方案提高了 4-10dB。
[Abstract]:The traditional secret image sharing technology needs to process all the data of the image. Because of the large amount of image data, the execution time of the algorithm is relatively long. Moreover, the total amount of data generated by the split storage algorithm is obviously expanded compared with the original image data volume, which will create a great burden on the network transmission and storage, when the image signal is sparse. Or most of the coefficients are zero or approximately 00:00 under the representation of a sparse transform basis. The compression sensing technique constructs a suitable measurement matrix which is not related to the sparse base to measure the original image. The measured data contain most of the useful information of the original image. At the same time, the original image can be compressed from high dimension to low dimension, which greatly reduces the amount of data to be processed. It can solve many problems caused by large amount of data in traditional methods. A precise or highly approximate approximation of the original image information can be obtained by a certain reconstruction algorithm. In this paper, the traditional secret image sharing and compression sensing techniques are combined. In order to further improve the performance of the scheme, a secret image sharing scheme based on compressed sensing technology is implemented. Through experiments, we find that the proposed scheme can significantly reduce the amount of data to be processed, and effectively reduce the execution time of the algorithm. And the reconstructed image can achieve an ideal visual effect. Experimental results show that: compared with the classical Thien-Lin scheme. The initial scheme in this paper can reduce the average time of image sharing by 48.66% and the time of image restoration by 29.32%. The optimized scheme can reduce the time of sharing by 58.77% and 58.16% on average. The restore time. Moreover, the reconstructed image precision of the optimized scheme is 4-10 dB higher than that of the original scheme.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

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本文編號(hào)：1488258