Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Li, Yongsong; Li, Zhengzhou; Wei, Kai; Xiong, Weiqi; Yu, Jiangpeng; Qi, Bo

doi:10.3390/s19020339

Cited by 13 publications

(6 citation statements)

References 41 publications

(59 reference statements)

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“…The MAD is a robust measure of sample deviation in univariate numerical data and is often used to screen out outliers in data [ 45 ]. The method primarily determines whether an item is an outlier by determining whether its deviation from the median value is within a reasonable range.…”

Section: Proposed Methodsmentioning

confidence: 99%

A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics

Xie,

Yi,

Yang

2023

Sensors

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Blind noise level estimation is a key issue in image processing applications that helps improve the visualization and perceptual quality of images. In this paper, we propose an improved block-based noise level estimation algorithm. The proposed algorithm first extracts homogenous patches from a single noisy image using local features, obtaining the covariance matrix eigenvalues of the patches, and constructs dynamic thresholds for outlier discrimination. By analyzing the correlations between scene complexity, noise strength, and other parameters, a nonlinear discriminant coefficient regression model is fitted to accurately predict the number of redundant dimensions and calculate the actual noise level according to the statistical properties of the elements in the redundancy dimension. The experimental results show that the accuracy and robustness of the proposed algorithm are better than those of the existing noise estimation algorithms in various scenes under different noise levels. It performs well overall in terms of performance and execution speed.

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Section: Proposed Methodsmentioning

confidence: 99%

A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics

Xie,

Yi,

Yang

2023

Sensors

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“…where u T min I wm is the Euclidean norm of the vector u T min I wm , and u min is the minimum variance direction vector calculated using PCA and defined as the eigenvector associated with the minimum eigenvalue of the covariance matrix given in (20).…”

Section: Proposed Noise Parameter Estimation Modelmentioning

confidence: 99%

“…Past studies have engineered various noise parameter estimations methods to adopt the Poisson-Gaussian signal-dependent noise model for CMOS image sensors and achieved satisfactory results. One kind is based on deep learning, which depends on the ability of the convolutional neural network in memorizing training data [20][21][22][23][24][25]. The other is based on calculation from single image.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Poisson–Gaussian Signal-Dependent Noise from Single Image of CMOS/CCD Image Sensor Using Local Binary Cyclic Jumping

Zhang

et al. 2021

Sensors

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Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.

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“…Zhang et al, detect homogeneous regions in wavelet transformed blocks, and combine them together to create a larger sample set for the variance estimation of mixed Poissonian-Gaussian noise [16]. Li et al, select homogenous blocks via local gray statistic entropy [17]. Haar wavelet-based local median absolute deviation and maximum likelihood estimation are applied to the homogenous blocks to estimate the noise parameters.…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression

Sun

Nian

2020

Remote Sensing

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HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR (multiple linear regression) is proposed for the above mixed noise to estimate the noise standard deviation of both SI component and SD component. First, superpixel segmentation is performed on the first principal component obtained by MNF (minimum noise fraction)-based dimensionality reduction to generate non-overlapping regions with similar pixels. Then, MLR is performed to remove the spectral correlation, and a system of linear equations with respect to noise variances is established according to the local sample statistics calculated within each superpixel. By solving the equations in terms of the least-squares method, the noise variances are determined. The experimental results show that the proposed algorithm provides more accurate local sample statistics, and yields a more accurate noise estimation than the other state-of-the-art algorithms for simulated HSIs. The results of the real-life data also verify the effectiveness of the proposed algorithm.

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Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Cited by 13 publications

References 41 publications

A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics

A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics

Parameter Estimation of Poisson–Gaussian Signal-Dependent Noise from Single Image of CMOS/CCD Image Sensor Using Local Binary Cyclic Jumping

Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression

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