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Type: Journal article
Title: MPTV: matching pursuit based total variation minimization for image deconvolution
Author: Gong, D.
Tan, M.
Shi, Q.
van den Hengel, A.
Zhang, Y.
Citation: IEEE Transactions on Image Processing, 2019; 28(4):1851-1865
Publisher: IEEE
Issue Date: 2019
ISSN: 1057-7149
Statement of
Dong Gong, Mingkui Tan, Qinfeng Shi, Anton van den Hengel, and Yanning Zhang
Abstract: Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, however, often suffer from the over-smoothing issue and solution bias caused by the homogeneous penalization. In this paper, we consider addressing these issues by applying inhomogeneous regularization on different image components. We formulate the inhomogeneous TV minimization problem as a convex quadratic constrained linear programming problem. Relying on this new model, we propose a matching pursuit based total variation minimization method (MPTV), specifically for image deconvolution. The proposed MPTV method is essentially a cuttingplane method, which iteratively activates a subset of nonzero image gradients, and then solves a subproblem focusing on those activated gradients only. Compared to existing methods, MPTV is less sensitive to the choice of the trade-off parameter between data fitting and regularization. Moreover, the inhomogeneity of MPTV alleviates the over-smoothing and ringing artifacts, and improves the robustness to errors in blur kernel. Extensive experiments on different tasks demonstrate the superiority of the proposed method over the current state-of-the-art.
Keywords: Total variation; image deconvolution; matching pursuit; convex programming
Description: Published online 2018
Rights: © 2018 IEEE
RMID: 0030100661
DOI: 10.1109/TIP.2018.2875352
Appears in Collections:Australian Institute for Machine Learning publications

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