Abstract
Wavelet transform has the good characteristic of time-frequency locality and many researches show that it can perform well for denoising in smooth and singular areas. But it isn’t suitable for describing the signals, which have high dimensional singularities. Curvelet is one of new multiscale transform theories, which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing directions of edges in image. So it has superiority in some image analysis, such as image denoising. This paper proposes a new method for denoising, which combines curvelet transform and wavelet transform. The experiment indicates that this method has better performance.
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Li, Y., Zhang, S., Hu, J. (2010). Combining Curvelet Transform and Wavelet Transform for Image Denoising. In: Huang, DS., Zhang, X., Reyes García, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_40
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DOI: https://doi.org/10.1007/978-3-642-14932-0_40
Publisher Name: Springer, Berlin, Heidelberg
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