An Adaptive Method for Recovering Image from Mixed Noisy Data

Authors:
Jun Liu;Zhongdan Huan;Haiyang Huang;Haili Zhang
Affiliations:
School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing, People's Republic of China 100875;School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing, People's Republic of China 100875;School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing, People's Republic of China 100875;School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing, People's Republic of China 100875
Venue:
International Journal of Computer Vision
Year:
2009

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Abstract

In this paper, we present a new version of the famous Rudin-Osher-Fatemi (ROF) model to restore image. The key point of the model is that it could reconstruct images with blur and non-uniformly distributed noise. We develop this approach by adding several statistical control parameters to the cost functional, and these parameters could be adaptively determined by the given observed image. In this way, we could adaptively balance the performance of the fit-to-data term and the regularization term. The Numerical experiments have demonstrated the significant effectiveness and robustness of our model in restoring blurred images with mixed Gaussian noise or salt-and-pepper noise.