Postprocessing and denoising of video using sparse multiresolutional transforms

Osman G. Sezer; Onur G. Guleryuz

doi:10.1117/12.863018

7 September 2010 Postprocessing and denoising of video using sparse multiresolutional transforms

Osman G. Sezer, Onur G. Guleryuz

Proceedings Volume 7798, Applications of Digital Image Processing XXXIII; 77981M (2010) https://doi.org/10.1117/12.863018
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

Abstract

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.

Citation Download Citation

Osman G. Sezer and 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); https://doi.org/10.1117/12.863018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available