Abstract
In this paper, we propose a novel derivative augmented Lagrangian method for fast total variation (TV) based image restoration (TVIR). By introducing a novel variable splitting method, TVIR is approximately reformulated in the derivative space, resulting in a constrained convex optimization problem which is simple to solve. Then, we propose a derivative alternating direction method of multipliers (D-ADMM) to solve the derivative space image restoration problem. Furthermore, we provide a Fourier domain updating algorithm which can save two fast Fourier transform (FFT) operations per iteration. Experimental results show that, compared with the state-of-the-art algorithms, D-ADMM is more efficient and can achieve satisfactory restoration quality.
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Ren, D., Zuo, W., Zhang, H., Zhang, D. (2013). A Derivative Augmented Lagrangian Method for Fast Total Variation Based Image Restoration. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_37
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DOI: https://doi.org/10.1007/978-3-642-42057-3_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
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