The connection machine
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kalman filtering theory
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection by associative mapping
Pattern Recognition
Digital Picture Processing
Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Car detection in aerial thermal images by local and global evidence accumulation
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
ACM SIGGRAPH 2007 courses
Automatic noise estimation in images using local statistics. Additive and multiplicative cases
Image and Vision Computing
Detection of noise in digital images by using the averaging filter name COV
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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A blind noise variance algorithm that recovers the variance of noise in two steps is proposed. The sample variances are computed for square cells tessellating the noise image. Several tessellations are applied with the size of the cells increasing fourfold for consecutive tessellations. The four smallest sample variance values are retained for each tessellation and combined through an outlier analysis into one estimate. The different tessellations thus yield a variance estimate sequence. The value of the noise variance is determined from this variance estimate sequence. The blind noise variance algorithm is applied to 500 noisy 256*256 images. In 98% of the cases, the relative estimation error was less than 0.2 with an average error of 0.06. Application of the algorithm to differently sized images is also discussed.