Image interpolation using wavelet based hidden Markov trees

Authors:
K. Kinebuchi;D. D. Muresan;T. W. Parks
Affiliations:
Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA;-;-
Venue:
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Year:
2001

Citing 0
Cited 5

Image resolution enhancement via data-driven parametric models in the wavelet space

Journal on Image and Video Processing
Undecimated wavelet transform-based image interpolation

ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Ant colony optimization for wavelet-based image interpolation using a three-component exponential mixture model

Expert Systems with Applications: An International Journal
Improved iterative techniques to compensate for interpolation distortions

Signal Processing
Eigentransformation-based face super-resolution in the wavelet domain

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hidden Markov trees in the wavelet domain are capable of accurately modeling the statistical behavior of real world signals by exploiting relationships between coefficients in different scales. The model is used to interpolate images by predicting coefficients at finer scales. Various optimizations and post-processing steps are also investigated to determine their effect on the performance of the interpolation. The interpolation algorithm was found to produce noticeably sharper images with PSNR values which outperform many other interpolation techniques on a variety of images.