Regularized Deblurring using Directional Prior with Sparse Representation
Subhajit Dhar1, Vivek Maik2, Mayank Srivastava3
1Subhajit Dhar, Department of Electronics and Communication Engineering, S.R.M. Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Mayank Srivastava, Department of Electronics and Communication Engineering, S.R.M. Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Vivek Maik, Department of Electronics and Communication Engineering, S.R.M. Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 14 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 206-210 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10401291S319/19©BEIESP | DOI: 10.35940/ijeat.A1040.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Blind deconvolution defined as simultaneous estimation and removal of blur is an ill-posed problem that can be solved with well-posed priors. In this paper we focus on directional edge prior based on orientation of gradients. Then the deconvolution problem is modeled as L2-regularized optimization problem which seeks a solution through constraint optimization. The constrained optimization problem is done in frequency domain with an Augmented Lagrangian Method (ALM). The proposed algorithm is tested on various synthetic as well as real data taken from various sources and the performance comparison is carried out with other state of the art existing methods.
Keywords: Deblurring, Restoration, Sparse Prior, Gradient Angle Prior.
Scope of the Article: Knowledge Representation and Retrievals