Unsupervised estimation of signal-dependent CCD camera noise

Abstract

This article deals with an original method to estimate the noise introduced by optical imaging systems, such as CCD cameras. The power of the signal-dependent photon noise is decoupled from the power of the signal-independent electronic noise. The method relies on the multivariate regression of sample mean and variance. Statistically similar image pixels, not necessarily connected, produce scatterpoints that are clustered along a straight line, whose slope and intercept measure the signal-dependent and signal-independent components of the noise power, respectively. Experimental results carried out on a simulated noisy image and on true data from a commercial CCD camera highlight the accuracy of the proposed method and its applicability to separate R-G-B components that have been corrected for the nonlinear effects of the camera response function, but not yet interpolated to the the full size of the mosaiced R-G-B image.

Indirect Estimation of Signal-Dependent Noise With Nonadaptive Heterogeneous Samples

“…There are different methods for partitioning the image, with different complexity and accuracy. The partition can be made, e.g., by simply using pixels extracted from a sufficiently small window from the noisy image [9], with the constraint that the intensity does not change much within the window [7], [8], or by segmenting the image into level sets (bins) with individual intensity values [1], [6], [10], [11], [13]. More sophisticated techniques, such as DCT-based estimators [12], have been also proposed.…”

Gaussian-Cauchy mixture modeling for robust signal-dependent noise estimation

“…The common procedure [1][2][3][4][5][6][7][8] for signal-dependent noise estimation consists in dividing the data or image of interest into uniform or homogeneous regions. Each group of samples is then used for the estimation of a mean-standard deviation pairs.…”

Advanced statistical tools for enhanced quality digital imaging with realistic capture models

“…The contribution by Aiazzi et al [10] revisits optoelectronic noise model in view of recent advances in the technology of modern CCD color cameras. The authors present an original and robust method for estimating the parameters of a mixed photon and electronic noise from a single image.…”

“…The negative effects of noise can be largely suppressed by post-processing algorithms, but these require precise knowledge of the noise characteristics to achieve optimal performance. Due to the mismatch between the actual noise characteristics and typically assumed, simplified noise models, the noise level is typically over-or underestimated, affecting adversely the performance of noise reduction.Three papers in this special issue [9][10][11] address the problem of camera noise modeling and different aspects of noise reduction, ranging from post-processing steps for hardware solutions to purely signal processing postproduction approaches. Available technologies for reducing noise in hardware include pixel binning [12,13], where electrical charges of neighboring pixels are combined to form a superpixel.…”

“…Three papers in this special issue [9][10][11] address the problem of camera noise modeling and different aspects of noise reduction, ranging from post-processing steps for hardware solutions to purely signal processing postproduction approaches. Available technologies for reducing noise in hardware include pixel binning [12,13], where electrical charges of neighboring pixels are combined to form a superpixel.…”