EURASIP Journal on Advances in Signal Processing
Simultaneous MAP-based video denoising and rate-distortion optimized video encoding
IEEE Transactions on Circuits and Systems for Video Technology
Patch-based video processing: a variational Bayesian approach
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
A new fuzzy motion and detail adaptive video filter
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Fast Multi-Hypothesis Motion Compensated Filter for Video Denoising
Journal of Signal Processing Systems
A fuzzy filter for the removal of random impulse noise in image sequences
Image and Vision Computing
Using the higher order singular value decomposition for video denoising
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Denoising of medical images using a reconstruction-average mechanism
Digital Signal Processing
A Proposed Intelligent Denoising Technique for Spatial Video Denoising for Real-Time Applications
International Journal of Mobile Computing and Multimedia Communications
Arbitrarily shaped virtual-object based video compression
Multimedia Tools and Applications
| Hi-index | 0.00 |
A combined spatial- and temporal-domain wavelet shrinkage algorithm for video denoising is presented in this paper. The spatial-domain denoising technique is a selective wavelet shrinkage method which uses a two-threshold criteria to exploit the geometry of the wavelet subbands of each video frame, and each frame of the image sequence is spatially denoised independently of one another. The temporal-domain denoising technique is a selective wavelet shrinkage method which estimates the level of noise corruption as well as the amount of motion in the image sequence. The amount of noise is estimated to determine how much filtering is needed in the temporal-domain, and the amount of motion is taken into consideration to determine the degree of similarity between consecutive frames. The similarity affects how much noise removal is possible using temporal-domain processing. Using motion and noise level estimates, a video denoising technique is established which is robust to various levels of noise corruption and various levels of motion.