採用適應性支持權重方法之 光場影像深度估測;Depth Estimation Using Adaptive Support-Weight Approach for Light Field Images

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/77409

Title:	採用適應性支持權重方法之光場影像深度估測;Depth Estimation Using Adaptive Support-Weight Approach for Light Field Images
Authors:	張博一;Chang, Po-Yi
Contributors:	通訊工程學系
Keywords:	光場影像;視差估測;適應性支持權重方法;子像素精確度;十字型視窗;light field images;disparity estimation;adaptive support-weight;sub-pixel accuracy;cross window
Date:	2018-07-26
Issue Date:	2018-08-31 14:37:15 (UTC+8)
Publisher:	國立中央大學
Abstract:	光場相機(Light field camera)以透鏡陣列(microlens array)擷取多視角影像，由於多視角之間的基線較窄，因此在計算視差時需精確至小數位之視差值。適應性支持權重方法為一種局部性的視差估測方案，雖現有文獻曾使用適應性支持權重方法(adaptive support-weight approach)對光場影像進行視差估測，但未考慮估測視差之子像素精確度(sub-pixel accuracy)。因此，本論文提出基於改進的適應性支持權重方法(adaptive support-weight approach)之光場影像深度估測方案。在計算視差前，先對輸入之各視角的光場影像進行雙立方內插(bicubic interpolation)，再以適應性支持權重方法(adaptive support-weight approach)計算視差，以提高視差估測之精確度。於計算適應性支持權重之匹配代價時，本論文採用十字形視窗以降低計算複雜度(computation complexity)，並動態調整十字形視窗之交錯位置，以估測影像邊界的視差值，此外，加重影像中變化激烈處之像素點之適應性支持權重，以提高視差估測之準確率。最後，結合所有微透鏡陣列(microlens array)之同一水平位置的各視角之估測視差的值，以提高視差估測之準確性。實驗結果顯示，本論文所提出之方案，相較於現有於極平面影像(epipolar plane image)基於適應性視窗之方案，於new HCI dataset之training子集，平均上可降低5.4%之視差估測之錯誤率(badpixel)。 ;Light field cameras acquire muti-view images using the microlens array. Due to the narrow baseline between multiple views, sub-pixel accuracy of the estimated disparity is expected. Adaptive support-weight approach is a local based disparity estimation method. Although several adaptive support-weight approach (ASW) based disparity estimation schemes for light field images have been proposed, they did not consider the problem of sub-pixel accuracy. Therefore, this thesis proposes to improve adaptive support-weight approach based depth estimation for light field images. Before disparity estimation, bicubic interpolation is applied to light field images for sub-pixel accuracy. Then the adaptive support-weight approach estimates disparities, where the cross window is adopted to reduce computation complexity. The intersection position of the vertical and horizontal arms is dynamically adjusted on the image border. Then, we increase the weights of pixels which have higher edge response. Finally, the estimated disparities from multiple view featuring with the same horizontal position are combined to generate the disparity map of the central view. Our experimental results show that the average error rate of the proposed method is lower than that of the EPI based adaptive window matching approach for 5.4%.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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