International Journal of Computer Science Issues

More than a traditional journal...

Removing the noise from an image is vitally important in many real-world computer vision applications. One of the most effective method is block matching collaborative filter- ing, which employs low-rank approximation to the group of similar patches gathered by searching from the noisy image. However, the main drawback of this method is that the stan- dard deviation of noises within the image is assumed to be known in advance, which is impossible for many real appli- cations. In this paper, we propose a non-local filtering method by using the low-rank tensor decomposition method. For ten- sor decomposition, we choose CP model as the underlying low-rank approximation. Since we assume the noise variance is unknown and need to be learned from data itself, we em- ploy the Bayesian CP factorization that can learn CP-rank as well as noise variance solely from the observed noisy tensor data, The experimental results on image and MRI denoising demonstrate the superiorities of our method in terms of flex- ibility and performance, as compared to other tensor-based denoising methods.