Image denoising with complex ridgelets
Pattern Recognition
Image denoising using neighbouring wavelet coefficients
Integrated Computer-Aided Engineering
A multivariate thresholding technique for image denoising using multiwavelets
EURASIP Journal on Applied Signal Processing
Image Denoising Using Three Scales of Wavelet Coefficients
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Image Denoising Using Neighbouring Contourlet Coefficients
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Image denoising with neighbour dependency and customized wavelet and threshold
Pattern Recognition
The dynamic textures for water synthesis based on statistical modeling
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
Convolution wavelet packet transform and its applications to signal processing
Digital Signal Processing
Denoising of three dimensional data cube using bivariate wavelet shrinking
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Evolution-enhanced multiscale overcomplete dictionaries learning for image denoising
Engineering Applications of Artificial Intelligence
| Hi-index | 35.68 |
Translation invariant (TI) single wavelet denoising was developed by Coifman and Donoho (1994), and they show that TI is better than non-TI single wavelet denoising. On the other other hand, Strela et al. (1994) have found that non-TI multiwavelet denoising gives better results than non-TI single wavelets. We extend Coifman and Donoho's TI single wavelet denoising scheme to multiwavelets. Experimental results show that TI multiwavelet denoising is better than the single case for soft thresholding