一种新的非下采样Contourlet域图像去噪算法

作为新型高维奇异性分析工具,非下采样轮廓(Nonsubsampled Contourlet)变换不仅克服了小波(Wavelet)变换的非奇异性最优基缺点,而且提供了优于轮廓(Contourlet)变换的平移不变性.以性能优越的非下采样轮廓变换为基础,提出了一种新的图像去噪方法.该方法首先对图像进行非下采样轮廓变换,以得到不同尺度、不同方向上的变换系数;然后结合噪声分布特点确定多尺度阈值,并依此阚值对高频系数进行去噪处理;最后对去噪处理后的变换系数进行反变换,以得到去噪图像.仿真实验结果表明,该方法不仅拥有较强的抑制噪声的能力,而且具有较好的边缘保护能力,同时消除了图像边缘附近的伪吉布斯(Gibbs)现象,整体性能优于小波变换图像去噪和轮廓变换图像去噪方法.

Image Denoising Using Nonsampled Contourlet Transform and Muiti-scale Thresholds

The nonsubsampled contourlet transform is a fully shift invariant, multi-scale, and multi-direction expansion that has better directional frequency localization and a fast implementation. We proposed a novel image denoising method by incorporating the nonsubsampled contourlet transform. The fully shift invariant property and the high directional sensitivity of the nonsubsampled contourlet transform make the new method a very good choice for image denoising.Firstly, the image was decomposed in different subbands of frequency and orientation responses using the nonsubsampled contourlet transform. Then the multi-scale thresholds were computed according to noise distribution, and used to shrink the nonsubsampled contourlet coefficients. Finally, the modified nonsubsampled contourlet coefficients were transformed back into the original domain to get the denoised image. Simulation results show that the method can obtain higher peak-signal-to-noise ratio, compared with other recent image denoising methods, such as wavelet denoising and contourlet denoising.