Image Representation Via a Finite Radon Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete analytical Ridgelet transform
Signal Processing - Signal processing in communications
Generalized Discrete Radon Transforms and Their Use in the Ridgelet Transform
Journal of Mathematical Imaging and Vision
A New Digital Implementation of Ridgelet Transform for Images of Dyadic Length
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Texture Classification Using Ridgelet Transform
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Ridgelet transform applied to motion compensated images
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
The discrete periodic Radon transform
IEEE Transactions on Signal Processing
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
The fast discrete Radon transform. I. Theory
IEEE Transactions on Image Processing
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In this paper, an energy-based adaptive transform scheme in the discrete periodic Radon transform domain is proposed for an efficient representation of linear singularities in images. Experimental results using non-linear approximation show that it possesses the superior property of energy concentration compared with the discrete wavelet transform and finite ridgelet transform. Furthermore, we have applied the scheme to the denoising problem and proposed a novel threshold selection method. Results of our experimental work, carried out on images containing strong linear singularities and texture components with varying levels of additive white Gaussian noise, show that our approach achieves a substantial improvement in terms of both signal-to-noise ratio and in visual quality as compared with that of the discrete wavelet transform and finite ridgelet transform.