Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representation

Authors:
Jens Krommweh
Affiliations:
Department of Mathematics, University of Duisburg-Essen, Campus Duisburg, 47048 Duisburg, Germany
Venue:
Journal of Visual Communication and Image Representation
Year:
2010

Citing 6
Cited 5

Efficient Moment Computation over Polygonal Domains with an Application to Rapid Wedgelet Approximation

SIAM Journal on Scientific Computing
Tetrolet shrinkage with anisotropic total variation minimization for image approximation

Signal Processing
Sparse geometric image representations with bandelets

IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation

IEEE Transactions on Image Processing
Adaptive Directional Lifting-Based Wavelet Transform for Image Coding

IEEE Transactions on Image Processing
Direction-Adaptive Discrete Wavelet Transform for Image Compression

IEEE Transactions on Image Processing

Tetrolet shrinkage with anisotropic total variation minimization for image approximation

Signal Processing
Image quality assessment measure based on natural image statistics in the tetrolet domain

ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Color local complexity estimation based steganographic (CLCES) method

Expert Systems with Applications: An International Journal
New image restoration method associated with tetrolets shrinkage and weighted anisotropic total variation

Signal Processing
Tetrolet regularization and learning for single frame image super-resolution

IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to get an efficient image representation we introduce a new adaptive Haar wavelet transform, called Tetrolet Transform. Tetrolets are Haar-type wavelets whose supports are tetrominoes which are shapes made by connecting four equal-sized squares. The corresponding fast filter bank algorithm is simple but very effective. In every level of the filter bank algorithm we divide the low-pass image into 4x4 blocks. Then in each block we determine a local tetrolet basis which is adapted to the image geometry in this block. An analysis of the adaptivity costs leads to modified versions of our method. Numerical results show the strong efficiency of the tetrolet transform for image approximation.