Abstract
This paper proposes a computationally fast scheme for denoising a video sequence. Temporal processing is done separately from spatial processing and the two are then combined to get the denoised frame. The temporal redundancy is exploited using a scalar state 1-D Kalman filter. A novel way is proposed to estimate the variance of the state noise from the noisy frames. The spatial redundancy is exploited using an adaptive edge-preserving Wiener filter. These two estimates are then combined using simple averaging to get the final denoised frame. Simulation results for the foreman, trevor and susie sequences show an improvement of 6 to 8 dB in PSNR over the noisy frames at PSNR of 28 and 24 dB.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE International Conference on Image Processing |
| Publisher | IEEE |
| Pages | 152-156 |
| Number of pages | 5 |
| Volume | 4 |
| State | Published - 1999 |
| Event | International Conference on Image Processing (ICIP'99) - Kobe, Jpn Duration: Oct 24 1999 → Oct 28 1999 |
Other
| Other | International Conference on Image Processing (ICIP'99) |
|---|---|
| City | Kobe, Jpn |
| Period | 10/24/99 → 10/28/99 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Electrical and Electronic Engineering