IEEE Trans Image Process - A multiplicative iterative algorithm for box-constrained penalized likelihood image restoration.

Tópicos

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Resumo

Image restoration is a computationally intensive problem as a large number of pixel values have to be determined. Since the pixel values of digital images can attain only a finite number of values (e.g., 8-bit images can have only 256 gray levels), one would like to recover an image within some dynamic range. This leads to the imposition of box constraints on the pixel values. The traditional gradient projection methods for constrained optimization can be used to impose box constraints, but they may suffer from either slow convergence or repeated searching for active sets in each iteration. In this paper, we develop a new box-constrained multiplicative iterative (BCMI) algorithm for box-constrained image restoration. The BCMI algorithm just requires pixelwise updates in each iteration, and there is no need to invert any matrices. We give the convergence proof of this algorithm and apply it to total variation image restoration problems, where the observed blurry images contain Poisson, Gaussian, or salt-and-pepper noises.

Resumo Limpo

imag restor comput intens problem larg number pixel valu determin sinc pixel valu digit imag can attain finit number valu eg bit imag can gray level one like recov imag within dynam rang lead imposit box constraint pixel valu tradit gradient project method constrain optim can use impos box constraint may suffer either slow converg repeat search activ set iter paper develop new boxconstrain multipl iter bcmi algorithm boxconstrain imag restor bcmi algorithm just requir pixelwis updat iter need invert matric give converg proof algorithm appli total variat imag restor problem observ blurri imag contain poisson gaussian saltandpepp nois

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