Swarm Intelligence for Additive White Gaussian Noise Level Estimation

Abstract

This paper presents a simple technique for estimating the noise levels in noisy images corrupted by additive white Gaussian noise. The proposed technique modifies the existing singular-valuedecomposition-based noise level estimation method. The proposed method calculates the sum of trailing singular values to infer noise levels. Particle swarm optimization and its variants can be used compute the optimal scalar value for the proposed noise level estimation method over a set of training images. As discussed in the experimental section, the proposed method outperforms existing schemes on noise level estimation tasks. Additionally, the estimated noise obtained from the proposed method can be used to improve the quality of denoised images.

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

“…To further improve the accuracy of the estimator, Turajlic [31] and Khmag [32] use the SVD domain to obtain the initial estimate and build a quadratic adjustment based on the original estimate. To improve the adaptability of the algorithm, Prasetyo uses particle swarm optimization [33] and symbiotic search techniques [34] respectively to optimize the scaling factor between the mean value of tail singular values and the noise STD. Liu [35] proposes a robust estimation algorithm for multilevel analysis of noise STDs in small regions.…”