In this paper, we propose an efficient noise robust edge detection technique based on odd Gaussian derivations in the wavelet transform domain. At first, new basis wavelet functions are introduced and the proposed algorithm is explained. The algorithm consists of two st
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In this paper, we propose an efficient noise robust edge detection technique based on odd Gaussian derivations in the wavelet transform domain. At first, new basis wavelet functions are introduced and the proposed algorithm is explained. The algorithm consists of two stage. The first idea comes from the response multiplication across the derivation and the second one is pruning algorithm which improves fake edges. Our method is applied to the binary and the natural grayscale image in the noise-free and the noisy condition with the different power density. The results are compared with the traditional wavelet edge detection method in the visual and the statistical data in the relevant tables. With the proper selection of the wavelet basis function, an admissible edge response to the significant inhibited noise without the smoothing technique is obtained, and some of the edge detection criteria are improved. The experimental visual and statistical results of studying images show that our method is feasibly strong and has good edge detection performances, in particular, in the high noise contaminated condition. Moreover, to have a better result and improve edge detection criteria, a pruning algorithm as a post processing stage is introduced and applied to the binary and grayscale images. The obtained results, verify that the proposed scheme can detect reasonable edge features and dilute the noise effect properly.
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