원문정보
초록
영어
Images will produce noise in the process of storage and collection. Wavelet threshold de-noising is a simple and effective de-noising method, but the choice of threshold function is a key. The hard-threshold function is discontinuous and there is the deviation between the signal processed by the soft-threshold function and the real signal, so this paper constructs a new threshold function at the origin sufficiently smooth to deal with above problems. A parameter is added to the new threshold function, which is between the soft-threshold and hard-threshold function by adjusting the parameter. The new threshold function can remove the noise effectively, and the image information is well preserved. Hence it plays an important role in follow-up edge detection. The de-noising method with improved wavelet threshold is presented, and then uses morphological edge detection on the new image in this paper. The result shows that the method can detect the complete edge effectively, and the visual effect and objective evaluation are good.
목차
1. Introduction
2. Improved Threshold De-Noising Function
2.1. Wavelet Threshold De-Noising
2.2. Hard and Soft Threshold
2.3. The Improved-Threshold Method
2.4. The Simulation Experiments
3. Edge Detection
3.1. Classical Edge Detection Based on Mathematical Morphology
3.2. Improved Operator of Morphology in Edge Detection
3.3. The Simulation Experiments
4. Conclusions
Acknowledgements
References