By Topic

Robust Soft-Decision Interpolation Using Weighted Least Squares

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kwok-Wai Hung ; Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., Hong Kong, China ; Wan-Chi Siu,

Soft-decision adaptive interpolation (SAI) provides a powerful framework for image interpolation. The robustness of SAI can be further improved by using weighted least-squares estimation, instead of least-squares estimation in both of the parameter estimation and data estimation steps. To address the mismatch issue of “geometric duality” during parameter estimation, the residuals (prediction errors) are weighted according to the geometric similarity between the pixel of interest and the residuals. The robustness of data estimation can be improved by modeling the weights of residuals with the well-known bilateral filter. Experimental results show that there is a 0.25-dB increase in peak signal-to-noise ratio (PSNR) for a sample set of natural images after the suggested improvements are incorporated into the original SAI. The proposed algorithm produces the highest quality in terms of PSNR and subjective quality among sophisticated algorithms in the literature.

Published in:

Image Processing, IEEE Transactions on  (Volume:21 ,  Issue: 3 )