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논문 기본 정보

자료유형
학술대회자료
저자정보
Zouwei Shen (National University of Singapore) Kim-Chuan Toh (National University of Singapore) Sangwoon Yun (고등과학원)
저널정보
한국산업응용수학회 한국산업응용수학회 학술대회 논문집 한국산업응용수학회 학술대회 논문집 Vol.5 No.2
발행연도
2010.12
수록면
115 - 118 (4page)

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Frame-based image restoration have been developed over the last decade. Many recently developed algorithms for image restoration can be viewed as an acceleration of the proximal forward-backward splitting algorithm. Accelerated proximal gradient algorithms studied by Nesterov, Nemirovski, and others have been demonstrated as efficient methods to solve various regularized convex optimization problems arising in compressive sensing, machine learning, and control. In this paper, we adapt the accelerated proximal gradient algorithm to solve the balanced approach model, in frame-based image restoration, which can be formulated as the ℓ₁-regularized least squares problem. This algorithm terminates in O(L/√?.) iterations with an ?-optimal solution and Lipschitz constant L, and gives a set of new frame-based image restoration algorithms that can cover several topics in image restorations, such as image deblurring, denoising, inpainting, and cartoon-texture image decomposition. The numerical results suggest that our algorithm is efficient and robust in solving large-scale image restoration problems. The algorithms we implemented are able to restore 512 × 512 images in various image restoration problems in less than 50 seconds on a modest PC. We also compare the numerical performance of our proposed algorithms applied to image problems by using one frame-based system with that by using cartoon and texture systems for image deblurring, denoising, and inpainting.

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UCI(KEPA) : I410-ECN-0101-2013-410-000845937