Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||MPTV: matching pursuit based total variation minimization for image deconvolution|
van den Hengel, A.
|Citation:||IEEE Transactions on Image Processing, 2019; 28(4):1851-1865|
|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|
|Appears in Collections:||Australian Institute for Machine Learning publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.