鬼成像像質(zhì)增強方法研究及超分辨實現(xiàn)
發(fā)布時間：2019-01-23 21:15
【摘要】：近年來,鬼成像這種新型的計算成像技術(shù)引起了越來越廣泛的關(guān)注,與傳統(tǒng)光學(xué)成像相比具有某些獨特的成像優(yōu)勢。但是其成像分辨率一直是制約該技術(shù)發(fā)展的一個主要問題。為了促進鬼成像能夠早日從實驗階段走向?qū)嵱没?本文針對如何改善鬼成像的成像質(zhì)量以及實現(xiàn)超分辨率成像做出研究。本文的工作主要是從改善鬼成像質(zhì)量的算法出發(fā),分析鬼成像計算模型的特點,尋求提高鬼成像質(zhì)量的有效途徑。主要內(nèi)容如下:探究壓縮感知五種經(jīng)典重構(gòu)算法,驗證其可以在少量測量條件下恢復(fù)圖像的可實行性。構(gòu)建基于壓縮感知的計算鬼成像模型,進行仿真實驗。分別選用離散余弦變換矩陣和高斯隨機矩陣作為稀疏基底和觀測矩陣。合理選擇壓縮采樣比和光場自由傳播距離,并用傅里葉變換對光場傳播建立數(shù)學(xué)模型。從而建立完整的基于壓縮感知的計算式鬼成像系統(tǒng)。針對該系統(tǒng)鬼成像重構(gòu)圖像中存在的噪聲問題,提出了BM3D算法與鬼成像相結(jié)合的計算方法。BM3D算法通過對圖像自身相似塊組合濾波,可有效去除原始算法帶來的某些誤差。設(shè)置相同實驗參數(shù)進行對比實驗,結(jié)果表明該方法有效提高了重構(gòu)圖像的峰值信噪比和視覺效果。最后分析了BM3D算法在應(yīng)用到鬼成像問題中的適用范圍�；诙喾沓上駡D像的超分辨重構(gòu)。對同一物體從6個不同角度分別進行采集。通過SIFT算法對6幅圖像配準,從圖像中提取出對應(yīng)同一場景的信息并匹配到同一幅圖像中,然后通過自適應(yīng)插值的方法進行超分辨重構(gòu)。結(jié)果表明該方法得到的圖像大部分區(qū)域有良好的視覺改善效果,原本不夠清晰的信息得以分辨。但是鬼成像自身恢復(fù)圖像時模糊嚴重的像素?zé)o法在關(guān)鍵點索引時被提取,一定程度上影響了超分辨重構(gòu)的結(jié)果。最后分析了兩種改善成像質(zhì)量方法各自的優(yōu)缺點,提出了改進意見,對鬼成像質(zhì)量提高的研究與實際應(yīng)用發(fā)展做出展望。
[Abstract]:In recent years, ghost imaging, a new computational imaging technology, has attracted more and more attention. Compared with traditional optical imaging, it has some unique imaging advantages. However, the imaging resolution has been a major problem restricting the development of the technology. In order to promote the application of ghost imaging from experimental stage to practical stage, this paper studies how to improve the imaging quality of ghost imaging and how to realize super-resolution imaging. The main work of this paper is to find an effective way to improve the quality of ghost imaging by analyzing the characteristics of the calculation model of ghost imaging from the point of view of the algorithm to improve the quality of ghost imaging. The main contents are as follows: five classical reconstruction algorithms of compression perception are explored to verify the practicability of image restoration under a few measurement conditions. A computational ghost imaging model based on compression perception is constructed and simulated. Discrete cosine transform matrix and Gao Si random matrix are used as sparse base and observation matrix respectively. The compression sampling ratio and the free propagation distance of light field are reasonably selected, and the mathematical model of light field propagation is established by Fourier transform. Thus, a complete computational ghost imaging system based on compression perception is established. In order to solve the noise problem in the reconstructed image of ghost imaging system, a method of combining BM3D algorithm with ghost image is proposed. By filtering the image with its own similar blocks, BM3D algorithm can effectively remove some errors caused by the original algorithm. The experimental results show that the proposed method can effectively improve the PSNR and visual effect of reconstructed images. Finally, the application range of BM3D algorithm in ghost imaging problem is analyzed. Superresolution reconstruction based on multiple ghost images. The same object was collected from six different angles. Six images are registered by SIFT algorithm. The information corresponding to the same scene is extracted from the image and matched to the same image. Then the super-resolution reconstruction is carried out by adaptive interpolation. The results show that most of the images obtained by this method have a good visual improvement effect, and the original information is not clear enough to distinguish. However, the pixels with serious blur can not be extracted when the key points are indexed, which affects the result of super-resolution reconstruction to some extent. Finally, the advantages and disadvantages of the two methods to improve the imaging quality are analyzed, and the improvement suggestions are put forward, and the prospects for the research and practical application of improving the quality of ghost imaging are made.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;O431.2
本文編號：2414198
[Abstract]:In recent years, ghost imaging, a new computational imaging technology, has attracted more and more attention. Compared with traditional optical imaging, it has some unique imaging advantages. However, the imaging resolution has been a major problem restricting the development of the technology. In order to promote the application of ghost imaging from experimental stage to practical stage, this paper studies how to improve the imaging quality of ghost imaging and how to realize super-resolution imaging. The main work of this paper is to find an effective way to improve the quality of ghost imaging by analyzing the characteristics of the calculation model of ghost imaging from the point of view of the algorithm to improve the quality of ghost imaging. The main contents are as follows: five classical reconstruction algorithms of compression perception are explored to verify the practicability of image restoration under a few measurement conditions. A computational ghost imaging model based on compression perception is constructed and simulated. Discrete cosine transform matrix and Gao Si random matrix are used as sparse base and observation matrix respectively. The compression sampling ratio and the free propagation distance of light field are reasonably selected, and the mathematical model of light field propagation is established by Fourier transform. Thus, a complete computational ghost imaging system based on compression perception is established. In order to solve the noise problem in the reconstructed image of ghost imaging system, a method of combining BM3D algorithm with ghost image is proposed. By filtering the image with its own similar blocks, BM3D algorithm can effectively remove some errors caused by the original algorithm. The experimental results show that the proposed method can effectively improve the PSNR and visual effect of reconstructed images. Finally, the application range of BM3D algorithm in ghost imaging problem is analyzed. Superresolution reconstruction based on multiple ghost images. The same object was collected from six different angles. Six images are registered by SIFT algorithm. The information corresponding to the same scene is extracted from the image and matched to the same image. Then the super-resolution reconstruction is carried out by adaptive interpolation. The results show that most of the images obtained by this method have a good visual improvement effect, and the original information is not clear enough to distinguish. However, the pixels with serious blur can not be extracted when the key points are indexed, which affects the result of super-resolution reconstruction to some extent. Finally, the advantages and disadvantages of the two methods to improve the imaging quality are analyzed, and the improvement suggestions are put forward, and the prospects for the research and practical application of improving the quality of ghost imaging are made.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;O431.2
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