本文選題：光場 + 壓縮感知��； 參考：《鄭州大學》2017年碩士論文

【摘要】：光場圖像包含豐富的空間3D信息,因此光場圖像可以用于重聚焦、深度估計以及三維顯示,其中精確的深度信息對顯著性檢測、超分辨重建、目標識別及3D表面重建等技術(shù)的發(fā)展具有重要的作用。雖然相機陣列和微透鏡陣列光場圖像獲取方法能夠有效的記錄光場圖像,但相機陣列方法由于體積大、成本高使應(yīng)用受到限制;微透鏡陣列方法是以犧牲圖像的空間分辨率換取角度分辨率。因此采用掩膜方法進行高分辨率光場采集與重建,并以重建光場圖像為基礎(chǔ)完成深度估計。本文的主要研究內(nèi)容如下:(1)研究壓縮感知的基本原理與掩膜光場相機的對應(yīng)關(guān)系,把壓縮感知原理應(yīng)用到光場重建中。壓縮感知理論中信號的稀疏表示是能夠重建的前提,為此詳細闡述了K-SVD算法原理,用K-SVD算法訓練光場樣本集獲取光場過完備字典,滿足光場圖像的理想稀疏表示,以更好的重建光場圖像。(2)對隨機測量矩陣優(yōu)化,滿足光場的物理重建需要。對優(yōu)化的隨機測量矩陣進行仿真光場圖像的采集與重建,證明此方法可以重建高空間分辨率和大角度分辨率的光場圖像。在此基礎(chǔ)上搭建基于掩摸的物理光場采集平臺,研究真實采集平臺下物理掩摸-投影矩陣-測量矩陣的轉(zhuǎn)換關(guān)系,完成掩摸到測量矩陣變換。最后通過光場過完備字典、掩膜轉(zhuǎn)換得到的測量矩陣、CCD編碼采樣圖結(jié)合壓縮感知重建算法,實現(xiàn)真實光場的物理采集和重建。為真實光場圖像的獲取提供一種簡單、有效的方法。(3)分析光場圖像重聚焦原理,用光場角度像素塊移動求和替代復雜積分實現(xiàn)光場圖像重聚焦。為更好的估計遮擋邊緣的深度信息,本文先通過分析研究光場圖像特點,對存在遮擋部分的角度像素塊分割優(yōu)化,以解決深度估計時光場圖像遮擋的問題。然后采用邊緣輪廓信息優(yōu)化深度圖,相比遮擋線索優(yōu)化深度圖的方法,本文方在保證計算精度同時降低了算法的復雜度。最后將重建的光場圖像進行重聚焦與深度估計。
[Abstract]:The light field image contains abundant spatial 3D information, so the light field image can be used for refocusing, depth estimation and 3D display, in which accurate depth information is used to detect salience, super-resolution reconstruction, etc. The development of target recognition and 3D surface reconstruction plays an important role. Although the method of obtaining light field image of camera array and microlens array can record the light field image effectively, the application of camera array method is limited because of its large volume and high cost. The method of microlens array is to sacrifice the spatial resolution of the image for the angular resolution. Therefore, the high resolution light field acquisition and reconstruction are carried out by mask method, and the depth estimation is completed based on the reconstructed light field image. The main contents of this paper are as follows: (1) the relationship between the basic principle of compression sensing and the mask light field camera is studied, and the principle of compression sensing is applied to the reconstruction of light field. The sparse representation of signal in compressed sensing theory is the premise of reconstruction. The principle of K-SVD algorithm is expounded in detail. The K-SVD algorithm is used to train the sample set of light field to obtain the over-complete dictionary of light field to satisfy the ideal sparse representation of light field image. The random measurement matrix is optimized with better reconstruction of light field image to meet the physical reconstruction needs of light field. The acquisition and reconstruction of simulated light field images based on the optimized random measurement matrix show that this method can reconstruct high spatial resolution and large angle resolution light field images. On this basis, the physical light field acquisition platform based on mask is built, and the conversion relationship between physical mask and projection matrix and measurement matrix is studied under the real acquisition platform, and the mask to measurement matrix transformation is completed. Finally, the physical acquisition and reconstruction of the real light field are realized through the over-complete dictionary of light field and the measurement matrix CCD coded sampling image obtained by mask conversion combined with the compressed perceptual reconstruction algorithm. This paper provides a simple and effective method for obtaining real light field image. It analyzes the principle of light field image refocusing and realizes the refocusing of light field image by moving the pixel block of the light field angle instead of the complex integral. In order to better estimate the depth information of occlusion edge, this paper analyzes the characteristics of light field image, and optimizes the segmentation of angle pixel block with occlusion part in order to solve the problem of depth estimation of time field image occlusion. Then, the edge contour information is used to optimize the depth map. Compared with the method of shading cues to optimize the depth map, the computational accuracy is guaranteed and the complexity of the algorithm is reduced. Finally, the reconstructed light field image is refocused and depth estimation is carried out.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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