本文選題：壓縮感知 + 測(cè)量矩陣��； 參考：《天津大學(xué)》2014年碩士論文

【摘要】：壓縮感知中,傳統(tǒng)的測(cè)量矩陣對(duì)圖像進(jìn)行單一采樣率的壓縮采樣,在信號(hào)的獲取和重構(gòu)過程中起著重要的作用。傳統(tǒng)的隨機(jī)測(cè)量矩陣在采樣率較高的情況下,能夠獲得比較好的重構(gòu)效果,但因采樣數(shù)目較多,故而資源耗費(fèi)也較多。確定性測(cè)量矩陣自身存在一些限制因素,與隨機(jī)測(cè)量矩陣相比,重構(gòu)效果不夠理想。為了解決上述問題,提出了兩種結(jié)構(gòu)隨機(jī)矩陣和多層分塊自適應(yīng)編碼算法�；趶V義輪換矩陣,對(duì)其循環(huán)基礎(chǔ)和循環(huán)構(gòu)造過程中所生成的每一行向量的第一個(gè)元素進(jìn)行改進(jìn),提出了兩種結(jié)構(gòu)隨機(jī)矩陣:廣義二進(jìn)制輪換矩陣和偽隨機(jī)廣義二進(jìn)制輪換矩陣。相對(duì)于傳統(tǒng)的測(cè)量矩陣,新的測(cè)量矩陣在二維圖像重建方面效果較好,所需重構(gòu)時(shí)間相差不大,在較低的采樣率下能夠獲得更加精確的重建�；诜謮KOSTM的自適應(yīng)分塊壓縮感知算法,提出了多層分塊自適應(yīng)編碼算法以及多層分塊自適應(yīng)壓縮感知編解碼方法。多層分塊自適應(yīng)壓縮感知編解碼方法基于多層分塊自適應(yīng)編碼算法,能夠根據(jù)圖像局部結(jié)構(gòu)進(jìn)行不同層數(shù)和大小的分塊,并自適應(yīng)分配采樣率。在同等重構(gòu)性能的前提下,相比較于單一采樣率下的壓縮感知,新的編解碼方法能夠不同程度地降低重構(gòu)圖像所需的采樣數(shù)目;相比于基于分塊OSTM的自適應(yīng)分塊壓縮感知算法,所提出的編解碼方法突破了其對(duì)矩陣要求的限制,在處理具有較大面積平滑圖像塊的圖像方面有著一定的優(yōu)勢(shì)。結(jié)構(gòu)隨機(jī)矩陣與自適應(yīng)分塊壓縮感知算法在實(shí)際應(yīng)用中有著廣闊的前景,值得進(jìn)一步深入研究。
[Abstract]:In compression sensing, the traditional measurement matrix performs compression sampling at a single sampling rate, which plays an important role in the process of signal acquisition and reconstruction. The traditional random measurement matrix can obtain better reconstruction effect under the condition of high sampling rate, but because of the large number of samples, the resources are consumed more. There are some limiting factors in deterministic measurement matrix. Compared with random measurement matrix, the reconstruction effect is not satisfactory. In order to solve the above problems, two kinds of structured random matrices and multi-layer block adaptive coding algorithms are proposed. Based on the generalized rotation matrix, the first element of each row vector generated in the cycle foundation and loop construction is improved. Two kinds of structured random matrices, generalized binary rotation matrix and pseudorandom generalized binary rotation matrix, are proposed. Compared with the traditional measurement matrix, the new measurement matrix has better effect in 2D image reconstruction, and the reconstruction time required is not different, so it can obtain more accurate reconstruction at lower sampling rate. An adaptive block compression sensing algorithm based on block OSTM is proposed in this paper. A multi-layer adaptive coding algorithm and a multi-layer block adaptive compression perceptual codec method are proposed. The multi-layer adaptive compression perceptual coding method is based on the multi-layer block adaptive coding algorithm. It can divide different layers and sizes according to the local structure of the image and allocate the sampling rate adaptively. On the premise of the same reconstruction performance, compared with the compression perception at a single sampling rate, the new coding and decoding method can reduce the number of samples needed for reconstructed image in varying degrees, compared with the adaptive block compression sensing algorithm based on block OSTM. The proposed coding and decoding method breaks through the limitation of matrix requirements and has some advantages in processing images with large area smooth image blocks. Structural random matrix and adaptive block compression sensing algorithm have a broad prospect in practical application, and deserve further research.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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