Super-resolution reconstruction of an image

Author

Elad, M. ; Feuer, A.

Author_Institution

Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel

fYear

1996

Firstpage

391

Lastpage

394

Abstract

This paper presents a generalization of restoration theory for the problem of super-resolution reconstruction (SRR) of an image. In the SRR problem, a set of low quality images is given, and a single improved quality image which fuses their information is required. We present a model for this problem, and show how the classic restoration theory tools-maximum likelihood estimator (ML), maximum a posteriori probability estimator (MAP) and the projection onto convex sets (POCS)-can be applied as a solution. A hybrid algorithm which joins the POCS and the ML benefits is suggested

Keywords

image resolution; image restoration; iterative methods; maximum likelihood estimation; MAP; ML; POCS; hybrid algorithm; image quality; image restoration theory tools; iterative two phase algorithm; low quality images; maximum a posteriori probability estimator; maximum likelihood estimator; projection onto convex sets; restoration theory; stochastic perception; superresolution image reconstruction; Additive noise; Equations; Fuses; Image reconstruction; Image resolution; Image restoration; Maximum likelihood estimation; Noise measurement; Pollution measurement; Size measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Electronics Engineers in Israel, 1996., Nineteenth Convention of

Conference_Location

Jerusalem

Print_ISBN

0-7803-3330-6

Type

conf

DOI

10.1109/EEIS.1996.566997

Filename

566997