An Improved Structure-Based Gaussian Noise Variance Estimation Method for Noisy Images

• Published in: Journal of Signal Processing Vol. 17; no. 6; pp. 299 - 305
• Main Authors: Shimamura, Tetsuya, Yi, Chong
• Format: Journal Article
• Language: English
• Published: Tokyo Research Institute of Signal Processing, Japan 2013; Japan Science and Technology Agency
• Subjects: Gaussian noise; homogeneity measure; image processing; noise variance estimation; structure-based algorithm
• ISSN: 1342-6230; 1880-1013; 1880-1013
• DOI: 10.2299/jsp.17.299

Noise can significantly impact the effectiveness of digital image processing. In this paper, an improved structure-based Gaussian noise variance estimation algorithm is presented. This method first separates the image into blocks and calculates homogeneity measures of every block through the proposed masks, taking the image structure into account. Then, the most homogeneous blocks are selected using a new threshold. Finally, pixel value variances of all selected blocks are averaged to estimate the global noise variance for one image. Comparative experiments with a variety of images using the proposed method and original structure-oriented method are described, and the experimental results show that the proposed method is feasible and effective, especially for good-quality images.