本文關(guān)鍵詞： 光譜成像 壓縮感知 結(jié)構(gòu)稀疏聚類 互補雙通道 GPU　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：光譜成像技術(shù)在軍事偵察、農(nóng)業(yè)生產(chǎn)、醫(yī)療診斷、科學(xué)研究等領(lǐng)域意義重大，然而傳統(tǒng)的光譜圖像獲取方法由于在時間分辨率、數(shù)據(jù)傳輸、系統(tǒng)開銷等方面存在較大壓力，因此難以滿足各領(lǐng)域的需求�？讖娇煺展庾V成像儀（Coded ApertureSnapshot Spectral Imagers, CASSI）是一種新型的基于壓縮感知理論的光譜成像系統(tǒng)（簡稱：壓縮光譜成像）。它可以通過一次曝光實現(xiàn)對一幀光譜場景的隨機混疊觀測，極大的降低了采樣速率、數(shù)據(jù)傳輸?shù)雀鞣矫娴膲毫Α５捎谒捎玫氖菃瓮ǖ赖膲嚎s觀測模型，損失了大量的光譜信息，很大程度上降低了信噪比，因此光譜圖像重構(gòu)質(zhì)量不盡如人意。 針對該問題，本文創(chuàng)新性地提出了基于互補編碼模板的雙通道光譜觀測模型，該模型充分利用每一幀場景的光譜能量和信息，提高了信噪比，實現(xiàn)了對光譜場景的互補觀測，使得光譜圖像重構(gòu)質(zhì)量得到較大提升。 另一方面，現(xiàn)有的光譜圖像重構(gòu)算法普遍采用固定變換空間作為稀疏域，難以很好地描述光譜圖像中的非局部結(jié)構(gòu)相似性，也未能利用光譜圖像的譜間相關(guān)性信息。針對這一問題，本文利用圖像的局部自相似性和非局部的自相似性提出針對光譜圖像的基于結(jié)構(gòu)稀疏聚類的表示模型。仿真實驗結(jié)果表明本文算法能獲得高質(zhì)量的光譜重構(gòu)效果。 基于結(jié)構(gòu)稀疏聚類的光譜重構(gòu)算法內(nèi)部包含多個子算法，計算復(fù)雜度較高，不利于光譜圖像重建的實時性。為此，本文又設(shè)計了基于GPU的并行計算方案，測試實驗表明了本文方案加速效果明顯，，且搭建系統(tǒng)框架簡單。
[Abstract]:Spectral imaging technology is of great significance in the fields of military reconnaissance, agricultural production, medical diagnosis, scientific research and so on. However, the traditional spectral image acquisition methods are under great pressure in terms of time resolution, data transmission, system overhead, etc. Therefore, it is difficult to meet the needs of various fields. The aperture snapshot spectral imager (CASSI) is a new type of spectral imaging system based on compression sensing theory (abbreviated as compressed spectral imaging). It can be realized by one exposure. Random aliasing observation of a spectral scene, The pressure of sampling rate and data transmission is greatly reduced. However, because it uses a single channel compression observation model, it loses a lot of spectral information, and greatly reduces the signal-to-noise ratio (SNR). Therefore, the quality of spectral image reconstruction is unsatisfactory. To solve this problem, a novel dual-channel spectral observation model based on complementary coding template is proposed in this paper. The model makes full use of the spectral energy and information of each frame scene, improves the signal-to-noise ratio, and realizes the complementary observation of the spectral scene. The quality of spectral image reconstruction is greatly improved. On the other hand, the existing spectral image reconstruction algorithms generally use fixed transform space as sparse domain, so it is difficult to describe the similarity of non-local structure in spectral image. The spectral correlation information of spectral images is also not used. In response to this problem, In this paper, a representation model based on structural sparse clustering for spectral images is proposed by using local self-similarity and non-local self-similarity of images. The simulation results show that the proposed algorithm can achieve high quality spectral reconstruction. The spectral reconstruction algorithm based on structural sparse clustering contains multiple subalgorithms, which has high computational complexity and is not conducive to real-time spectral image reconstruction. Therefore, a parallel computing scheme based on GPU is designed in this paper. The test results show that the acceleration effect of the scheme is obvious and the system framework is simple.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751;O433

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