Image signal-to-noise ratio estimation using Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average model.

Abstract:

:We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.

journal_name

Microsc Res Tech

authors

Sim KS,Wee MY,Lim WK

doi

10.1002/jemt.20610

subject

Has Abstract

pub_date

2008-10-01 00:00:00

pages

710-20

issue

10

eissn

1059-910X

issn

1097-0029

journal_volume

71

pub_type

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