Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data
Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data
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DOI：
10.1109/tip.2008.2001399
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发表时间：
2008-10-01
影响因子：
10.6
通讯作者：
Egiazarian, Karen
中科院分区：
文献类型：
--
作者：
Foi, Alessandro;Trimeche, Mejdi;Egiazarian, Karen
提出了一种简单实用的数字成像传感器原始数据噪声模型。该信号相关噪声模型给出了噪声的逐点标准偏差作为像素原始数据输出的期望的函数，该模型由泊松部分（对光子感测进行建模）和高斯部分（对于输出数据中的其余平稳干扰）组成。我们进一步明确地考虑到数据的削波（过度曝光和曝光不足），忠实地再现传感器的非线性响应。我们提出了一种算法，给出了一个单一的噪声图像的模型参数的全自动估计。合成图像和来自不同传感器的真实的原始数据的实验证明了该方法的实用性和所提出的模型的准确性。
We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data. We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.