IEEE Trans Image Process - Alternating direction method for balanced image restoration.

Tópicos

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Resumo

This paper presents an efficient algorithm for solving a balanced regularization problem in the frame-based image restoration. The balanced regularization is usually formulated as a minimization problem, involving an l(2) data-fidelity term, an l(1) regularizer on sparsity of frame coefficients, and a penalty on distance of sparse frame coefficients to the range of the frame operator. In image restoration, the balanced regularization approach bridges the synthesis-based and analysis-based approaches, and balances the fidelity, sparsity, and smoothness of the solution. Our proposed algorithm for solving the balanced optimal problem is based on a variable splitting strategy and the classical alternating direction method. This paper shows that the proposed algorithm is fast and efficient in solving the standard image restoration with balanced regularization. More precisely, a regularized version of the Hessian matrix of the l(2) data-fidelity term is involved, and by exploiting the related fast tight Parseval frame and the special structures of the observation matrices, the regularized Hessian matrix can perform quite efficiently for the frame-based standard image restoration applications, such as circular deconvolution in image deblurring and missing samples in image inpainting. Numerical simulations illustrate the efficiency of our proposed algorithm in the frame-based image restoration with balanced regularization.

Resumo Limpo

paper present effici algorithm solv balanc regular problem framebas imag restor balanc regular usual formul minim problem involv l datafidel term l regular sparsiti frame coeffici penalti distanc spars frame coeffici rang frame oper imag restor balanc regular approach bridg synthesisbas analysisbas approach balanc fidel sparsiti smooth solut propos algorithm solv balanc optim problem base variabl split strategi classic altern direct method paper show propos algorithm fast effici solv standard imag restor balanc regular precis regular version hessian matrix l datafidel term involv exploit relat fast tight parsev frame special structur observ matric regular hessian matrix can perform quit effici framebas standard imag restor applic circular deconvolut imag deblur miss sampl imag inpaint numer simul illustr effici propos algorithm framebas imag restor balanc regular

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