research-article

Noise Level Estimation for Natural Images Based on Scale-Invariant Kurtosis and Piecewise Stationarity

Authors:

Yuan Yan TangAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 26, Issue 2

Pages 1017 - 1030

https://doi.org/10.1109/TIP.2016.2639447

Published: 01 February 2017 Publication History

Abstract

Noise level estimation is crucial in many image processing applications, such as blind image denoising. In this paper, we propose a novel noise level estimation approach for natural images by jointly exploiting the piecewise stationarity and a regular property of the kurtosis in bandpass domains. We design a $K$ -means-based algorithm to adaptively partition an image into a series of non-overlapping regions, each of whose clean versions is assumed to be associated with a constant, but unknown kurtosis throughout scales. The noise level estimation is then cast into a problem to optimally fit this new kurtosis model. In addition, we develop a rectification scheme to further reduce the estimation bias through noise injection mechanism. Extensive experimental results show that our method can reliably estimate the noise level for a variety of noise types, and outperforms some state-of-the-art techniques, especially for non-Gaussian noises.

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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 26, Issue 2

February 2017

545 pages

ISSN:1057-7149

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IEEE Press

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Published: 01 February 2017

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Cited By

Yue GCheng DZhou THou JLiu WXu LWang TCheng J(2023)Perceptual Quality Assessment of Enhanced Colonoscopy Images: A Benchmark Dataset and an Objective MethodIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.326021233:10(5549-5561)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3260212
Sun WZhou JDong LTian JLiu J(2021)Optimal Pre-Filtering for Improving Facebook Shared ImagesIEEE Transactions on Image Processing10.1109/TIP.2021.309379430(6292-6306)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TIP.2021.3093794
Jiang PWang QWu J(2020)Efficient Noise-Level Estimation Based on Principal Image TextureIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.291231930:7(1987-1999)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1109/TCSVT.2019.2912319
Kokil PPratap T(2020)Additive White Gaussian Noise Level Estimation for Natural Images Using Linear Scale-Space FeaturesCircuits, Systems, and Signal Processing10.1007/s00034-020-01475-x40:1(353-374)Online publication date: 22-Jun-2020
https://dl.acm.org/doi/10.1007/s00034-020-01475-x
Fang ZYi X(2019)A novel natural image noise level estimation based on flat patches and local statisticsMultimedia Tools and Applications10.1007/s11042-018-7137-478:13(17337-17358)Online publication date: 2-Aug-2019
https://dl.acm.org/doi/10.1007/s11042-018-7137-4

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