Blind estimation of white Gaussian noise variance in highly textured images

TL;DR

This paper introduces a novel blind noise variance estimation method for highly textured images, utilizing DCT coefficients and iterative exclusion, achieving significantly improved accuracy over existing methods.

Contribution

The paper presents a new iterative DCT-based approach for blind noise variance estimation in highly textured images, with superior accuracy demonstrated on a new test database.

Findings

Approximately two times lower estimation RMSE than other methods.

Effective in highly textured images with various noise levels.

Validated on a newly designed image database.

Abstract

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.