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An image denoising method based on nonsubsampled contourlet transform with SQP optimization

In this paper, we propose an image denoising method based on nonsubsampled contourlet transform (NSCT) with successive quadratic programming (SQP) optimization. This method can obtain the optimal threshold for each subband without the priori information of the noise variance using SQP optimization and generalized cross validation (GCV) criterion. After the threshold is determined, a nonlinear threshold function is applied to overcome the inadequate of soft threshold and hard threshold function. The experimental results show that the proposed method has a better performance than other contourlet-based image denoising methods and outperforms on both visual quality and peak signal-to-noise ratio (PSNR).