ArticleSensors (Basel, Switzerland)2018
Parameter Estimation of Signal-Dependent Random Noise in CMOS/CCD Image Sensor Based on Numerical Characteristic of Mixed Poisson Noise Samples.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A Review of Neural Network-Based Image Noise Processing Methods.Sensors (Basel, Switzerland) · 2025Review
- Parameter Estimation of Poisson-Gaussian Signal-Dependent Noise from Single Image of CMOS/CCD Image Sensor Using Local Binary Cyclic Jumping.Sensors (Basel, Switzerland) · 2021Article
- A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes.Sensors (Basel, Switzerland) · 2019Article
- A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors.Sensors (Basel, Switzerland) · 2019Article
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3 authors.
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Abstract
Parameter estimation of Poisson-Gaussian signal-dependent random noise in the complementary metal-oxide semiconductor/charge-coupled device image sensor is a significant step in eliminating noise. The existing estimation algorithms, which are based on finding homogeneous regions, acquire the pair of the variances of noise and the intensities of every homogeneous region to fit the linear or piecewise linear curve and ascertain the noise parameters accordingly. In contrast to the existing algorithms, in this study, the Poisson noise samples of all homogeneous regions in every block image are pieced together to constitute a larger sample following the mixed Poisson noise distribution; then, the mean and variance of the mixed Poisson noise sample are deduced. Next, the mapping function among the noise parameters to be estimated-variance of Poisson-Gaussian noise and that of Gaussian noise corresponding to the stitched region in every block image-is constructed. Finally, the unbiased estimations of noise parameters are calculated from the mapping functions of all the image blocks. The experimental results confirm that the proposed method can obtain lower mean absolute error values of estimated noise parameters than the conventional ones.
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