Blur Kernel Estimation Using Color-Line Model For Natural Image Deblurring
Date Issued
2014
Date
2014
Author(s)
Lai, Wei-Sheng
Abstract
Image deblurring in a fundamental problem in computer vision and image processing. The goal of image deblurring is to recover blur kernels and latent images from input blurred images. It is an inverse problem similar to image denoising and image super-resolution, but more challenging due to the diversity of motion blur kernels and natural images. Recent deblurring approaches include two main stages: blur kernel estimation and image restoration. Blur kernel estimation algorithms rely on sufficient significant edges on input images to infer a reliable blur kernel. Most approaches use only Y layer in the YUV color space to estimate blur kernels. However, researches show that RGB-to-Y conversion may lose contrast and edge information.
In this thesis, we focus on solving single-image motion deblurring problem in which blurry images are produced by camera shakes with hand-held shots. A novel deblurring algorithm is proposed, which consider RGB color channels together in the blur kernel estimation stage. We also propose two color-image priors derived from the color-line model. Our color-image priors not only restore image contrasts around edges, but also reduce image noises caused by inaccurate estimated blur kernels during optimization. Experimental results show that our algorithm can enhance the stability and the accuracy of blur kernel estimation. Accurate blur kernels can reduce the ringing artifacts generated from deconvolution, and lead to better deblurred results. Our algorithm do not need any external learning information, but the performance of our algorithm is comparable to the learning-based method and outperforms other single-image blind deconvolution methods.
Subjects
影像去模糊
最佳化逆問題
盲反卷積問題
點擴散函數/運動模糊函數估計
色彩線模型
Type
thesis
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