非局部变分修复法去除高密度椒盐噪声

杨文波; 马天玮; 刘剑

doi:10.3788/CO.20130606.876

Volume 6 Issue 6

Dec. 2013

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Chinese Optics > 2013 > 6(6): 876-884.

YANG Wen-bo, MA Tian-wei, LIU Jian. Elimination of impulse noise by non-local variation inpainting method[J]. Chinese Optics, 2013, 6(6): 876-884. doi: 10.3788/CO.20130606.876

Citation:

YANG Wen-bo, MA Tian-wei, LIU Jian. Elimination of impulse noise by non-local variation inpainting method[J]. Chinese Optics, 2013, 6(6): 876-884. doi: 10.3788/CO.20130606.876

YANG Wen-bo, MA Tian-wei, LIU Jian. Elimination of impulse noise by non-local variation inpainting method[J]. Chinese Optics, 2013, 6(6): 876-884. doi: 10.3788/CO.20130606.876

Citation:

YANG Wen-bo, MA Tian-wei, LIU Jian. Elimination of impulse noise by non-local variation inpainting method[J]. Chinese Optics, 2013, 6(6): 876-884. doi: 10.3788/CO.20130606.876

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Elimination of impulse noise by non-local variation inpainting method

doi: 10.3788/CO.20130606.876

YANG Wen-bo^{1,2
,
,},
MA Tian-wei¹,
LIU Jian¹

1.
Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Received Date: 12 Sep 2013
Rev Recd Date: 15 Oct 2013
Publish Date: 10 Dec 2013

Abstract

Abstract

The reasons of ineffectiveness of median filtering and its improved algorithm for eliminating the high-density salt-and-pepper noise are analyzed. A variational inpainting method is adopted to remove the high-density salt-and-pepper noise, and a inpainting model of Non-local Total Variation(NL-TV) based on the existing model of Total Variation(TV) is proposed in this article. In the NL-TV model based on the characteristics of salt-and-pepper noise(uniform distribution and the gray value of 0 or 255), we view the noise points as the lost or damaged points of an image to find the districts similar to the neighborhoods of noise points in an image, and then interpolate the noise points by taking the central pixel in a similar district as a new neighborhood of noise points. By this method, we transform the problem of image denoising into a problem of image restoration to remove the high-density noise. The experimental results show that the Peak Signal to Noise Ratios(PSNRs) are 22.85 and 28.77 after removing the noise for a color and gray-scale image with 90% of noise density, which is better than the results obtained by median filter and its improved algorithm in terms of the objective evaluation criteria. Using this model, we can effectively remove the high-density salt-and-pepper noise and restore the image details better, which provides a new approach to remove the high-density noise.
- image denoising,
- image inpainting,
- salt and pepper noise,
- median filtering,
- non-local variational inpainting

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References

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