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Title BALLORG: State-of-the-art Image Restoration using Block-augmented Lagrangian and Low-rank Gradients
Authors (Laya Tojo) ; (Manju Devi) ; (Vivek Maik) ; (Gurushankar)
Page pp.1-8
ISSN 2287-5255
Keywords Image restoration; BALLORG; Low-rank Prior; Augmented Lagrangian; Penalty methods; Lagrangian multipliers; Derivative prior; Block sparsity; Ill posed optimization; Constrained optimization
Abstract In this paper, we propose a blind image deblurring algorithm using block-augmented Lagrangian and low-rank priors (BALLORG) as a non-learning method that can give better results without the complexity of learning-based methods. The proposed algorithm achieves faster convergence within 20 iterations than conventional methods. Regularization priors are used in the form of gradients and sparse low-rank matrices, and recursive rank improvements result in better deblurring performance. The steepest descent in minimization is maintained through weight selection for penalty and regularization parameters. The block processing introduces local and global optimization, leading to better visual quality outputs. The proposed method has excellent performance in terms of the PSNR, SSIM, and FSIM matrix, which is on par with or better than that of other state-of-the-art learning and non-learning-based approaches.