Removal of additive white noise using an adaptive Wiener filter with edge retention
✍ Scribed by Masato Tsukahara; Miki Haseyama; Hideo Kitajima
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 677 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0882-1666
No coin nor oath required. For personal study only.
✦ Synopsis
In this paper, an adaptive Wiener filter for removal of additive white noise is proposed. Images are partitioned into a set of blocks of pixels, divided into five subsets of blocks according to their edge contents and directions, namely, shade, horizontal, vertical, and two diagonal classes. Each subset of blocks is used to define a covariance matrix, from which a Wiener filter is derived. For classification of blocks in the presence of noise, five eigenvectors calculated from the five covariance matrices are used. For a block classified into the appropriate class, five inner products are calculated between five eigenvectors and the block in the presence of noise. After classification, by switching the Wiener filter according to the input block, edge-preserving image filtering is useful. Experimental results are included to verify the usefulness of the proposed method.