Image Deblurring Technologies for Large Images and Light Field Images
Date Issued
2016
Date
2016
Author(s)
Wang, Neng-Chien
Abstract
Image processing has been developed for a long time. This paper can be separated into two parts. We will introduce the proposed techniques of image deblurring at first. Then the proposed light field deblurring algorithm will be introduced. The literatures of image deblurring can be categorized into two classes: blind deconvolution and non-blind deconvolution. First, we try to improve the efficiency of non-blind deconvolution in ultra-high resolution images. The complexity of deblurring is raised in ultra-high resolution images. Therefore, we try to reduce the computation time. We modified the algorithm “Fast Image Deconvolution” proposed by Krishnan in 2009. To reduce complexity, we process the image in block, and find the optimal division that can minimize the complexity. Merging the result of each block directly will cause blocking effect, so it should be overlapped between sub-images with linear weight. The size of overlapping decided our computing time and performance. Less overlapping is more efficient but leads to worse performance. For balance, we choose a specific size of overlapping which give consideration both efficiency and performance. Another topic is light field deblurring. A light field camera can capture the location and the angle information from a single shot. Thereby, we can reconstruct the depth of scene and stereoscopic images can be obtained. A light field camera is composed by the array of lens. We will obtain sub-images by every lens. If we want to render the image, we have to obtain the disparity of each microimage pair and hence we can estimate the information of the depth. At first, we obtain the relationship among microlenses by using regression analysis. Then, we take white image into consideration to compensate the luminance of the edge of every microimage and use quad-trees to compute disparity more precisely. Moreover, we use the image-based rendering technique to improve the quality of the reconstructed image. After rendering image, we use the technique of image segmentation. Then, every object will be cut apart. We estimate the depth of every object by the disparity, and hence we can reconstruct the depth map of the whole image.
Subjects
deblurring
ultra-high resolution image
light field
quad-tree
image-based rendering
Type
thesis
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