A New Method for Choosing the Regularization Parameter of ROF Total Variation Image Denoising
Lan Zhang, Lei Xu · 2016
The total variation regularization denoising is a widely used denoising method. But the denoising effect of this method depends on the choice of the regularization parameter. In this paper, estimate parameter of ROF total variation denoising based on quantum particle swarm optimization (QPSO) algorithm is proposed. At per iteration step, we also fit a model about the optimal parameter and standard deviation of Gaussian noise. The experimental results show that the proposed method can obtain favourable denoising result and has a good performance in PSNR.