Proceedings of the IEEK Conference (대한전자공학회:학술대회논문집)
- 2003.07e
- /
- Pages.1711-1714
- /
- 2003
Spatially Adaptive High-Resolution Denoising Based on Nonstationary Correlation Assumption
비정적 상관관계를 고려한 공간적응적 잡음제거 알고리즘
Abstract
The noise in an image degrades image quality and deteriorates coding efficiency of compression. Recently, various edge-preserving noise filtering methods based on the nonstationary image model have been proposed to overcome this problem. In most conventional nonstationary image models, however, pixels are assumed to be uncorrelated to each other In order not to increase the computational burden too much. As a result, some detailed information is lost in the filtered results. In this paper, we propose a computationally feasible adaptive noise smoothing algorithm which considers the nonstationary correlation characteristics of images. We assume that an image has a nonstationary mean and can be segmented into subimages which have individually different stationary correlations. Taking advantage of the special structure of the covariance matrix that results from the proposed image model, we derive a computationally efficient FFT-based adaptive linear minimum mean square error filter. The justification for the proposed image model is presented and the effectiveness of the proposed algorithm is demonstrated experimentally.
Keywords