An edge detection method for strong noisy image using shearlets

Yuming Li; Hanqiang Cao; Zijian Xu

doi:10.1117/12.901901

8 December 2011 An edge detection method for strong noisy image using shearlets

Yuming Li, Hanqiang Cao, Zijian Xu

Proceedings Volume 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis; 800305 (2011) https://doi.org/10.1117/12.901901
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

Numerous edge detection methods have been proposed to detect image edges. However, these methods are not very effective in detecting edges in strong noisy images. Recent years, multiscale analysis has been introduced to the realm of image processing. As the third generation wavelet, shearlets have their own superiority. Anisotropic dilation operator and shear operator are introduced to overcome the shortcomings of traditional wavelets. Because of their sensitivity to directions, shearlets are apt to do the job of edge detection. Based on shearlets, in this paper, a new edge detection method is proposed. The main idea about this new method is combining the shearlet denoising method with the edge detecting method based on shearlets. Analyzing results show that edges are characterized as zerocrossing points in shearlet domain and can be extracted from shearlet transform coefficients by detecting zero crossing points and using boundary tracking method. Many experiments are conducted to test this novel approach and we also compare Sobel, Log and Canny operators with this new method. Experiments demonstrate that when an image existing high deviation Gaussian noise, this method are much better than ordinary edge detection operators in time domain.

Citation Download Citation

Yuming Li, Hanqiang Cao, and Zijian Xu "An edge detection method for strong noisy image using shearlets", Proc. SPIE 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis, 800305 (8 December 2011); https://doi.org/10.1117/12.901901

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KEYWORDS

Edge detection

Image processing

Denoising

Detection and tracking algorithms

Wavelets

Fourier transforms

Image fusion

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