Anisotropic Spatiotemporal Regularization in Compressive Video Recovery by Adaptively Modeling the Residual Errors as Correlated Noise
Eslahi, Nasser; Foi, Alessandro (2018-08-27)
Eslahi, Nasser
Foi, Alessandro
IEEE
27.08.2018
2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2018 - Proceedings
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201810292494
https://urn.fi/URN:NBN:fi:tty-201810292494
Kuvaus
Peer reviewed
Tiivistelmä
Many approaches to compressive video recovery proceed iteratively, treating the difference between the previous estimate and the ideal video as residual noise to be filtered. We go beyond the common white-noise modeling by adaptively modeling the residual as stationary spatiotemporally correlated noise. This adaptive noise model is updated at each iteration and is highly anisotropic in space and time; we leverage it with respect to the transform spectra of a motion-compensated video denoiser. Experimental results demonstrate that our proposed adaptive correlated noise model outperforms state-of-the-art methods both quantitatively and qualitatively.
Kokoelmat
- TUNICRIS-julkaisut [16726]