Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers
Publication in refereed journal



摘要The total variation (TV) model is attractive in that it is able to preserve sharp attributes in images. However, the restored images from TV-based methods do not usually stay in a given dynamic range, and hence projection is required to bring them back into the dynamic range for visual presentation or for storage in digital media. This will affect the accuracy of the restoration as the projected image will no longer be the minimizer of the given TV model. In this paper, we show that one can get much more accurate solutions by imposing box constraints on the TV models and solving the resulting constrained models. Our numerical results show that for some images where there are many pixels with values lying on the boundary of the dynamic range, the gain can be as great as 10.28 decibel in the peak signal-to-noise ratio. One traditional hindrance using the constrained model is that it is difficult to solve. However, in this paper, we propose using the alternating direction method of multipliers (ADMM) to solve the constrained models. This leads to a fast and convergent algorithm that is applicable for both Gaussian and impulse noise. Numerical results show that our ADMM algorithm is better than some state-of-the-art algorithms for unconstrained models in terms of both accuracy and robustness with respect to the regularization parameter.
著者Chan RH, Tao M, Yuan XM
期刊名稱SIAM Journal on Imaging Sciences
詳細描述To ORKTS:
頁次680 - 697
關鍵詞alternating direction method of multipliers; box constraint; deblurring; total variation
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING; Imaging Science & Photographic Technology; IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY; Mathematics; Mathematics, Applied; MATHEMATICS, APPLIED

上次更新時間 2020-13-10 於 01:58