Quantile analysis of image sensor noise distribution

Zhang, Jiachao; Hirakawa, Keigo; Jin, Xiaomin

doi:10.1109/icassp.2015.7178240

Cited by 7 publications

(6 citation statements)

References 28 publications

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“…In the traditional reinforcement learning algorithm for image denoising, reward function is usually derived from Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity (SSIM) [ 44 ]. In this study, the likelihood function of mixed Poisson–Gaussian noise, which was approximately solved in the previous studies [ 5 , 47 , 48 ], is introduced as the reward function without approximation. Specifically, the reward function is showing as follows:

…”

Section: Methodsmentioning

confidence: 99%

“…As indicated in Equation (3), GAT method simplifies the mixed Poisson–Gaussian likelihood as a Gaussian-like distribution. Although it is able to provide an approximate solution to the likelihood function, the tails of variance stabilized coefficients distribution are still empirically longer than normality [ 48 ] (which means the variance of the transformed noise still depends on signal intensity). In this study, the mixed Poisson–Gaussian likelihood function is directly introduced in the reinforcement learning algorithm without approximation.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Frame Star Image Denoising Algorithm Based on Deep Reinforcement Learning and Mixed Poisson–Gaussian Likelihood

Xie

Zhang

Zheng

et al. 2020

Sensors

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Mixed Poisson–Gaussian noise exists in the star images and is difficult to be effectively suppressed via maximum likelihood estimation (MLE) method due to its complicated likelihood function. In this article, the MLE method is incorporated with a state-of-the-art machine learning algorithm in order to achieve accurate restoration results. By applying the mixed Poisson–Gaussian likelihood function as the reward function of a reinforcement learning algorithm, an agent is able to form the restored image that achieves the maximum value of the complex likelihood function through the Markov Decision Process (MDP). In order to provide the appropriate parameter settings of the denoising model, the key hyperparameters of the model and their influences on denoising results are tested through simulated experiments. The model is then compared with two existing star image denoising methods so as to verify its performance. The experiment results indicate that this algorithm based on reinforcement learning is able to suppress the mixed Poisson–Gaussian noise in the star image more accurately than the traditional MLE method, as well as the method based on the deep convolutional neural network (DCNN).

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…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi-Frame Star Image Denoising Algorithm Based on Deep Reinforcement Learning and Mixed Poisson–Gaussian Likelihood

Xie

Zhang

Zheng

et al. 2020

Sensors

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show abstract

Section: Introductionmentioning

confidence: 99%

“…In digital imaging systems, images are deteriorated by random noise coming from the complementary metal-oxide semiconductor/charge-coupled device (CMOS/CCD) image sensor [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Compared with the additive signal-independent noise model, the signal-dependent noise model is more accurate at characterizing random noise of the CMOS/CCD image sensor [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Most researchers assumed that the signal-dependent noise model of the digital imaging sensor is cond as a Poisson-Gaussian noise model and the validity of the Poisson–Gaussian noise model was certified by CMOS sensors from Nokia camera phones, CCD sensors from Fujifilm cameras and CMOS sensors from Canon cameras [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the additive signal-independent noise model, the signal-dependent noise model is more accurate at characterizing random noise of the CMOS/CCD image sensor [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Most researchers assumed that the signal-dependent noise model of the digital imaging sensor is cond as a Poisson-Gaussian noise model and the validity of the Poisson–Gaussian noise model was certified by CMOS sensors from Nokia camera phones, CCD sensors from Fujifilm cameras and CMOS sensors from Canon cameras [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. The Poisson-Gaussian noise model is composed of a signal-dependent term accounting for photon noise (Poisson) and a signal-independent term accounting for the remaining noise in the readout data (Gaussian) [ 1 ], as shown in (1).…”

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation of Signal-Dependent Random Noise in CMOS/CCD Image Sensor Based on Numerical Characteristic of Mixed Poisson Noise Samples

Zhang

Wang

2018

Sensors

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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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Infrared star image denoising using regions with deep reinforcement learning

Zhang

Zheng

et al. 2021

Infrared Physics & Technology

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Quantile analysis of image sensor noise distribution

Cited by 7 publications

References 28 publications

Multi-Frame Star Image Denoising Algorithm Based on Deep Reinforcement Learning and Mixed Poisson–Gaussian Likelihood

Multi-Frame Star Image Denoising Algorithm Based on Deep Reinforcement Learning and Mixed Poisson–Gaussian Likelihood

Parameter Estimation of Signal-Dependent Random Noise in CMOS/CCD Image Sensor Based on Numerical Characteristic of Mixed Poisson Noise Samples

Infrared star image denoising using regions with deep reinforcement learning

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