Edge Detection in Presence of Impulse Noise - Advances in Image and Graphics Technologies - page 17

4 Concluding Remarks
We first present a modified Mumford-shah model with L 1 fidelitytermbyintro-
ducing one L 1 -norm term to approximate the edge length, and show an ecient
algorithm based on the split Bregman iteration to solve the minimum prob-
lem. Comparing with the seminar Ambrosio-Tortorelli model which introduces
a quadratic integral of an edge signature to approximate the edge length, we de-
sign the gradient descent algorithm. Numerical experimental results show that
our approximation of the edge length are robust, and our algorithm based on
split Bregman iteration are effective and accurate.
Acknowledgments. This research supported by NSFC (No. 11271126) and the
Fundamental Research Funds for the Central Universities.
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