An Adaptive PM Model Based on Difference Eigenvalue for Image Restoration

TIAN Haiying; CAI Hongmin; LAI Jianhuang

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(4): 761-766

TIAN Haiying, CAI Hongmin, LAI Jianhuang, “An Adaptive PM Model Based on Difference Eigenvalue for Image Restoration,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 761-766, 2014,

Citation:

TIAN Haiying, CAI Hongmin, LAI Jianhuang, “An Adaptive PM Model Based on Difference Eigenvalue for Image Restoration,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 761-766, 2014,

TIAN Haiying, CAI Hongmin, LAI Jianhuang, “An Adaptive PM Model Based on Difference Eigenvalue for Image Restoration,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 761-766, 2014,

Citation:

TIAN Haiying, CAI Hongmin, LAI Jianhuang, “An Adaptive PM Model Based on Difference Eigenvalue for Image Restoration,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 761-766, 2014,

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An Adaptive PM Model Based on Difference Eigenvalue for Image Restoration

1.
School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China;
2.
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China

Funds: This work is supported by National Natural Science Foundation of China (No.61173084, No.60902076) and National Science and Technology Pillar Program (No.2012BAK16B06).

Received Date: 2013-02-01
Rev Recd Date: 2013-09-01
Publish Date: 2014-10-05

Abstract

Abstract

Classic Perona-Malik (PM) model is usually used to smooth noise for the degraded image. However, to our knowledge, a well-known defect of the PM model is prone to cause ‘staircase’ effect and blur image fine feature because of the lack of correct diffusion strength in diffusion process. To tackle the problem, we will improve the PM model by developing a new structure descriptor to adjust anisotropic diffusion strength for smoothing noise and preserving image feature adaptively. The new structure descriptor is based on image local geometry-Hessian matrix, called difference eigenvalue, which can adaptively track edge feature and high degree of homogeneity in an image, even when the observed image is blurred. Experiments on both nature and medical images show that the improved PM method can achieve a superior performance than the traditional methods in terms of visual inspection and quantitative measurements.
- Hessian matrix,
- Structure descriptor,
- Anisotropic diffusion,
- Noise reduction

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References(22)

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