Video denoising by combining Kalman and Wiener estimates

Rakesh Dugad, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
PublisherIEEE
Pages152-156
Number of pages5
Volume4
StatePublished - 1999
EventInternational Conference on Image Processing (ICIP'99) - Kobe, Jpn
Duration: Oct 24 1999Oct 28 1999

Other

OtherInternational Conference on Image Processing (ICIP'99)
CityKobe, Jpn
Period10/24/9910/28/99

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Video denoising by combining Kalman and Wiener estimates'. Together they form a unique fingerprint.

Cite this