Noise Estimation for Images using Eigen Values and Frobenius norm (International Journal of Engineering and Computer Science, 2018)
Performance of image denoising algorithm is critically dependent on the accuracy of noise level estimation. In the estimation process, restricting the influence of image content on the estimation dataset is a major challenge. In this paper, we propose a novel method which involves the application of Frobenius norm as the basis of content energy measurement. It involves the truncation of SVD (Singular Value Decomposition) components thereby effectively restricting the influence of image details. Application of linear regression for determining content parameter enhances the application scope of the proposed method. The experimental results demonstrates the effectiveness of the proposed approach.