Digital Image Processing
Image denoising via lossy compression and wavelet thresholding
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Filtering random noise from deterministic signals via datacompression
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Wavelet-based image denoising using a Markov random field a priori model
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
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A new system of multi-scale transform, namely, the curvelets, was developed recently, which possess directional features and provides optimally sparse representation of objects with edges. In this paper a novice algorithm for image denoising based on lossy compression and curvelet thresholding (LCCT) is proposed. The results are compared with the results obtained from denoising methods like wavelets (DWT), lossy compression and wavelet thresholding (LCWT) and curvelets (DCvT). Standard deviation and PSNR are selected as performance metrics and it is shown that the proposed algorithm outperforms the existing algorithms.