Patch based fast noise level estimation using DCT and standard deviation

Mohan, S.; Raghavendiran, T. A.; Rajavel, R.

doi:10.1007/s10586-018-2327-4

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…Noise is then estimated on the high-frequencies. Mohan et al [16] first perform intra-image patch matching, then estimate noise in the discrete cosine transform (DCT) domain. Other methods use principal component analysis (PCA) to find homogeneous patches.…”

Section: Related Workmentioning

confidence: 99%

“…An accurate noise level estimate can significantly boost the performance of downstream applications such as video denoising [2], forgery detection [10], camera identification, camera characterization, and video quality assessment. Most noise estimation methods focus on single images [24,17,3,18,4,16,19,15]. These methods can be applied to each frame for video noise estimation, but video temporal redundancy is not used.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Signal-dependent Video Noise Estimator Via Inter-frame Signal Suppression

Li,

Gardella,

Bammey

et al. 2023

Image Processing on Line

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We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then the Ponomarenko et al. method is extended to video by sorting pairs by their low-frequency energy and estimating noise in the high frequencies. Experiments on a real dataset of drone videos show its performance for different parameter settings and different noise levels. Two extensions of the proposed method using subpixel matching and for multiscale noise estimation are respectively analyzed. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.md file of the archive.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Signal-dependent Video Noise Estimator Via Inter-frame Signal Suppression

Li,

Gardella,

Bammey

et al. 2023

Image Processing on Line

View full text Add to dashboard Cite

show abstract

“…Noise is then estimated on the high-frequencies. Mohan et al [8] first perform intra-image patch matching, then estimate noise in the discrete cosine transform (DCT) domain. Other methods use Principal Component Analysis (PCA) to find homogeneous patches.…”

Section: Related Workmentioning

confidence: 99%

Video Signal-Dependent Noise Estimation via Inter-Frame Prediction

Gardella

Bammey

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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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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Patch based fast noise level estimation using DCT and standard deviation

Cited by 8 publications

References 28 publications

A Signal-dependent Video Noise Estimator Via Inter-frame Signal Suppression

A Signal-dependent Video Noise Estimator Via Inter-frame Signal Suppression

Video Signal-Dependent Noise Estimation via Inter-Frame Prediction

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

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