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Robust destriping method with unidirectional total variation and framelet regularization
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DOI：10.1364/OE.21.023307.png)
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摘要
En 中文
Multidetector imaging systems often suffer from the problem of stripe noise and random noise, which greatly degrade the imaging quality. In this paper, we propose a variational destriping method that combines unidirectional total variation and framelet regularization. Total-variation-based regularizations are considered effective in removing different kinds of stripe noise, and framelet regularization can efficiently preserve the detail information. In essence, these two regularizations are complementary to each other. Moreover, the proposed method can also efficiently suppress random noise. The split Bregman iteration method is employed to solve the resulting minimization problem. Comparative results demonstrate that the proposed method significantly outperforms state-of-the-art destriping methods on both qualitative and quantitative assessments. (C) 2013 Optical Society of America
Keyword:
NONUNIFORMITY CORRECTION
MODIS DATA
WAVELET
STRIPE
NOISE
DECONVOLUTION
RESTORATION
TOMOGRAPHY
ALGORITHMS
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期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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