Noise Variance Estimation in Digital Images using Finite Differences Filter

Kowalski, J.; Mikołajczak, G.; Pęksiński, Jakub

doi:10.1109/tsp.2018.8441168

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Unlike traditional transform domains and statistical approaches, they can adapt to different noise distributions and scene structures. They can also be combined with air domain and frequency domain processing tools [ 34 , 35 ], providing greater flexibility and applicability. However, the method also has some challenges, such as selecting the appropriate patch size and image coverage and reducing the interference of the original image structure information in the noise estimation.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Xie,

Yi,

Yang

2023

Sensors

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show abstract

“…There are some methods to filter the image content signal and then treat the filtered image as noise. For example, Kowalski [9] utilizes the finite‐difference filter to remove the original image signal and then calculates the STD according to the retained noise information. However, the edge information of the image is often mistaken for noise, which greatly reduces the accuracy of the method.…”

Section: Introductionmentioning

confidence: 99%

Gaussian noise parameter estimation based on multiple singular value decomposition and non‐linear fitting

Sun

et al. 2022

IET Image Processing

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Modelling and estimate of the LTWM noise parameters

Prlinčević

Milivojević²

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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In the first part of this paper, a statistical analysis of the noise in the image, as a result of the insertion of the watermark by Liu and Tan algorithm, was performed. After that, using the base of test image the noise model was created. Based on the model, an algorithm for estimating the statistical parameters of noise, LTWM algorithm, (μ and σ 2) in the image with a watermark, was created. In the second part of this paper there is a description of the experiment in which a comparative analysis of the accuracy of estimating statistical parameters by the proposed LTWM algorithm and Algorithms from [3], [6] - [8], was performed. Algorithms have been applied on watermarked images from the NRCS base. The results of MAE and MSE between true and estimated variance are shown in tables and diagrams. In addition, the execution time of the tested algorithms was measured. A detailed comparative analysis has shown that the proposed LTWM algorithm was more precise, and has a shorter execution time, compared to the analysed algorithms.

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Noise Variance Estimation in Digital Images using Finite Differences Filter

Cited by 4 publications

References 16 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

Gaussian noise parameter estimation based on multiple singular value decomposition and non‐linear fitting

Modelling and estimate of the LTWM noise parameters

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