7 September 2010 Postprocessing and denoising of video using sparse multiresolutional transforms
Author Affiliations +
This paper describes the construction of a set of sparsity-distortion-optimized orthonormal transforms designed for wavelet-domain image denoising. The optimization operates over sub-bands of given orientation and exploits intra-scale dependencies of wavelet coefficients over image singularities. When applied on the top of standard wavelet transforms, the resulting new sparse representation provides compaction that can be exploited in transform domain denoising via cycle-spinning.1 Our construction deviates from the literature, which mainly focuses on model-based methods, by offering a data-driven optimization of wavelet representations. Compared with translational-invariant denoising, the proposed method consistently offers better performance compared to the original wavelet-representation and can reach up to 3dB improvements.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Osman G. Sezer, Osman G. Sezer, Onur G. Guleryuz, Onur G. Guleryuz, } "Postprocessing and denoising of video using sparse multiresolutional transforms", Proc. SPIE 7798, Applications of Digital Image Processing XXXIII, 77981M (7 September 2010); doi: 10.1117/12.863018; https://doi.org/10.1117/12.863018


Sparse Gabor wavelets by local operations
Proceedings of SPIE (June 28 2005)
Multicomposite wavelet estimation
Proceedings of SPIE (September 27 2011)
Wavelet detectors for model-based imaging
Proceedings of SPIE (July 27 1997)

Back to Top