Wavelet Based Image Denoising Using Adaptive Thresholding
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 03
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Adaptive lifting schemes with perfect reconstruction
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Nonlinear wavelet transforms for image coding via lifting
IEEE Transactions on Image Processing
Adaptive Directional Lifting-Based Wavelet Transform for Image Coding
IEEE Transactions on Image Processing
Distortion estimates for adaptive lifting transforms with noise
Image and Vision Computing
| Hi-index | 0.00 |
This paper proposes an image enhancement method based on space-adaptive, 2-D lifting scheme. In the space-adaptive update-first lifting scheme, the prediction stage is adapted to the signal structure point-by-point which results in a better signal representation and enhancement result. In this paper, a novel edge-sensitive adaptive prediction method is introduced in the 2-D lifting framework. The method adaptively chooses the best predictor among a set of predictors minimizing the prediction error. The proposed prediction method is sensitive to both even and odd indexed edge pixels in the 2-D lifting context. The bivariate shrinkage which assumes the dependence of the subband wavelet coefficients is used for subband image enhancement. As an objective quality measure, the peak signal-to-noise ratio test is applied to the results of the proposed image enhancement algorithm. Results of the proposed algorithm are compared with those of the VisuShrink, BayesShrink, and NorShrink. Experimental and objective quality test results prove the superior performance of the proposed image enhancement method.