A new adaptive algorithm for image denoising based on curvelet transform

Musheng Chen; Zhishan Cai

doi:10.1117/12.2032223

26 October 2013 A new adaptive algorithm for image denoising based on curvelet transform

Musheng Chen, Zhishan Cai

Proceedings Volume 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 89211N (2013) https://doi.org/10.1117/12.2032223
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

The purpose of this paper is to study a method of denoising images corrupted with additive white Gaussian noise. In this paper, the application of the time invariant discrete curvelet transform for noise reduction is considered. In curvelet transform, the frame elements are indexed by scale, orientation and location parameters. It is designed to represent edges and the singularities along curved paths more efficiently than the wavelet transform. Therefore, curvelet transform can get better results than wavelet method in image denoising. In general, image denoising imposes a compromise between noise reduction and preserving significant image details. To achieve a good performance in this respect, an efficient and adaptive image denoising method based on curvelet transform is presented in this paper. Firstly, the noisy image is decomposed into many levels to obtain different frequency sub-bands by curvelet transform. Secondly, efficient and adaptive threshold estimation based on generalized Gaussian distribution modeling of sub-band coefficients is used to remove the noisy coefficients. The choice of the threshold estimation is carried out by analyzing the standard deviation and threshold. Ultimately, invert the multi-scale decomposition to reconstruct the denoised image. Here, to prove the performance of the proposed method, the results are compared with other existent algorithms such as hard and soft threshold based on wavelet. The simulation results on several testing images indicate that the proposed method outperforms the other methods in peak signal to noise ratio and keeps better visual in edges information reservation as well. The results also suggest that curvelet transform can achieve a better performance than the wavelet transform in image denoising.

Citation Download Citation

Musheng Chen and Zhishan Cai "A new adaptive algorithm for image denoising based on curvelet transform", Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 89211N (26 October 2013); https://doi.org/10.1117/12.2032223

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KEYWORDS

Image denoising

Denoising

Wavelets

Wavelet transforms

Image processing

Image quality

Signal to noise ratio

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