Wavelet threshold denoising is widely used in the denoising of the infrared image for its simplicity and effectiveness in application. However, there has been a growing awareness to the observation that wavelets may not be the best choice for describing infrared images. This observation is due to the fact that wavelets are blind to the smoothness along the edges commonly found in images. A denoising method of infrared image based on Contourlet transform is presented in this paper. In selecting the hard threshold function to process the coefficients in the Contourlet domain, we could thereby obtain the denoised infrared image of superior quality via inverse transforming. The result of the experiment indicates that compared with the traditional algorithms of the wavelet, this method can preserve the detail and the texture of the infrared image more effectively, and has better image effect and the SNR value.
[1]
YI Wen-juan.
Contourlet:Efficient Directional and Multiresolution Analytic Tool
,
2006
.
[2]
R. Eslami,et al.
The contourlet transform for image denoising using cycle spinning
,
2003,
The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.
[3]
Martin Vetterli,et al.
Adaptive wavelet thresholding for image denoising and compression
,
2000,
IEEE Trans. Image Process..
[4]
Minh N. Do,et al.
Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation
,
2022
.