# Blind estimation of white Gaussian noise variance in highly textured images

arXiv: https://arxiv.org/abs/1711.10792 · HTML: https://mlsanity.com/paper/1711.10792/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [Artifact- and content-specific quality assessment for MRI with image rulers](https://mlsanity.com/paper/2111.03780/) (2021)
- [Learning-based Noise Component Map Estimation for Image Denoising](https://mlsanity.com/paper/2109.11877/) (2021)
- [Matrix-Valued LogSumExp Approximation for Colour Morphology](https://mlsanity.com/paper/2411.10141/) (2024)
- [Real-time Noise Source Estimation of a Camera System from an Image and Metadata](https://mlsanity.com/paper/2404.03251/) (2024)
- [Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer](https://mlsanity.com/paper/2409.06676/) (2024)
- [Colour Morphological Distance Ordering based on the Log-Exp-Supremum](https://mlsanity.com/paper/2503.11329/) (2025)

## Abstract (arXiv)

In the paper, a new method of blind estimation of noise variance in a single highly textured image is proposed. An input image is divided into 8x8 blocks and discrete cosine transform (DCT) is performed for each block. A part of 64 DCT coefficients with lowest energy calculated through all blocks is selected for further analysis. For the DCT coefficients, a robust estimate of noise variance is calculated. Corresponding to the obtained estimate, a part of blocks having very large values of local variance calculated only for the selected DCT coefficients are excluded from the further analysis. These two steps (estimation of noise variance and exclusion of blocks) are iteratively repeated three times. For the verification of the proposed method, a new noise-free test image database TAMPERE17 consisting of many highly textured images is designed. It is shown for this database and different values of noise variance from the set {25, 49, 100, 225}, that the proposed method provides approximately two times lower estimation root mean square error than other methods.