自然图像去模糊的非自然L0稀疏表示

Li Xu, Shicheng Zheng, Jiaya Jia
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引用次数: 989

摘要

我们在本文中表明,基于先前最大后验(MAP)的模糊去除方法的成功部分源于它们各自的中间步骤,这些步骤隐式或显式地创建了包含显著图像结构的非自然表示。我们提出了一种广义的、数学上合理的L0稀疏表达式,以及一种新的有效的运动去模糊方法。我们的系统在优化过程中不需要额外的滤波,并且显示出快速的能量下降,使得少量的迭代足以收敛。它还为均匀和非均匀运动去模糊提供了统一的框架。我们广泛地验证了我们的方法,并展示了与其他方法在收敛速度、运行时间和结果质量方面的比较。
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Unnatural L0 Sparse Representation for Natural Image Deblurring
We show in this paper that the success of previous maximum a posterior (MAP) based blur removal methods partly stems from their respective intermediate steps, which implicitly or explicitly create an unnatural representation containing salient image structures. We propose a generalized and mathematically sound L0 sparse expression, together with a new effective method, for motion deblurring. Our system does not require extra filtering during optimization and demonstrates fast energy decreasing, making a small number of iterations enough for convergence. It also provides a unified framework for both uniform and non-uniform motion deblurring. We extensively validate our method and show comparison with other approaches with respect to convergence speed, running time, and result quality.
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