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

Zhang, Yu; Wang, Guangyi; Xu, Jiangtao

doi:10.3390/s18072276

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…The radiometric response of a digital camera is the outcome of a number of factors, such as electromagnetic radiation, sensor electronics, the optical system, and so forth [51,52,53,54,55]. The noise present on a single image is basically composed of two components: the photoresponse noise of every sensor element (pixel) and the spatial nonuniformity or fixed pattern noise of the sensor array [56,57].…”

Section: Materials and Methodsmentioning

confidence: 99%

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

Molada-Tebar

Riutort-Mayol

Marqués-Mateu

et al. 2019

Sensors

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In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and sans-serifΔEab* color differences. Values of less than 3 CIELAB units were achieved for sans-serifΔEab*. The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and sans-serifΔEab*. We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.

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Section: Materials and Methodsmentioning

confidence: 99%

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

Molada-Tebar

Riutort-Mayol

Marqués-Mateu

et al. 2019

Sensors

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show abstract

“…However, images generated by these sensors inevitably contain noise, owing to their internal structure, which results in image quality degradation [5,6], and thus, estimating these noise parameters accurately assumes paramount importance in improving the performance of denoising algorithms [3,4,[7][8][9]. For CMOS image sensors, a signaldependent noise model, such as the Poisson-Gaussian model, can more accurately delineate the noise characteristics than an additive channel-dependent noise model [10][11][12][13][14][15][16][17][18][19].…”

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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“…Rakhshanfar et al, classify image patches according to their intensity and variance to find homogenous regions that represent the noise [15], and then clusters of connected patches are weighted and ranked to approximate the peak noise variance and noise level function. 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].…”

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

Cited by 12 publications

References 17 publications

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

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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