Total Variation (TV) is an effective and popular prior model in the field of regularization-based image processing. This paper focuses on TV for image restoration in the presence of impulse noise. This type of noise frequently arises in data acquisition and transmission due to many reasons, e.g. a faulty sensor or analog-to-digital converter errors. Removing this noise is an important task in image restoration. State-of-the-art methods such as Adaptive Outlier Pursuit(AOP), which is based on TV with L02-norm data fidelity, only give sub-optimal performance. In this paper, we propose a new method, called L0T V -PADMM, which solves the TV-based restoration problem with L0-norm data fidelity. To effectively deal with the resulting non-convex nonsmooth optimization problem, we first reformulate it as an equivalent MPEC (Mathematical Program with Equilibrium Constraints), and then solve it using a proximal Alternating Direction Method of Multipliers (PADMM). Our L0TV-PADMM method finds a desirable solution to the original L0-norm optimization problem and is proven to be convergent under mild conditions. We apply L0TV-PADMM to the problems of image denoising and deblurring in the presence of impulse noise. Our extensive experiments demonstrate that L0TV-PADMM outperforms state-of-the-art image restoration methods.
|Title of host publication
|2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
|Institute of Electrical and Electronics Engineers (IEEE)
|Number of pages
|Published - Oct 15 2015
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: IEEE Computer Society, Computer Vision Foundation - CVF