A novel total variation optimization method and its application on blind super-resolution

Author

Ting Li ; Papamichalis, Panos E.

Author_Institution

Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA

fYear

2014

fDate

27-30 Oct. 2014

Firstpage

3892

Lastpage

3896

Abstract

The total-variation (TV) regularization is very popular because of its ability to deal with noise while preserving important image features. This paper proposes a novel TV-based algorithm which can be applied to many inverse problems such as image de-convolution and super-resolution. The idea is to break the cost function into two parts: a linear part and a nonlinear part containing the TV term, then handling them one after the other in each iteration. This method has overall advantages considering both quality and speed. Then it is applied to blind super-resolution (SR), serving as the solver for both image estimation and the point spread function (PSF) estimation. The PSF estimation is also improved in this work by eliminating boundary pixel values that have not been computed from complete data. Synthetic and real data experiments show the nice performance of the proposed method.

Keywords

estimation theory; feature extraction; image resolution; iterative methods; optical transfer function; television applications; PSF estimation; TV-based algorithm; blind super-resolution; boundary pixel value elimination; cost function; image de-convolution; image estimation; image features; image super-resolution; inverse problems; iteration method; nonlinear part; point spread function estimation; total variation optimization; Equations; Estimation; Image resolution; Interpolation; Mathematical model; Noise; TV; Blind Super-Resolution; PSF estimation; Total Variation; inverse problem;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2014 IEEE International Conference on

Conference_Location

Paris

Type

conf

DOI

10.1109/ICIP.2014.7025790

Filename

7025790