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Related Experiment Video

Updated: Oct 5, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

A Study on Distortion Estimation Based on Image Gradients.

Sin Chee Chin¹, Chee-Onn Chow¹, Jeevan Kanesan¹

¹Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia.

Sensors (Basel, Switzerland)

|January 22, 2022

Summary

This summary is machine-generated.

This study introduces DE-G, a novel method for accurately estimating image noise parameters. DE-G effectively identifies additive, multiplicative, and impulsive noise from single images, advancing image denoising and quality assessment.

Keywords:

distortion estimation image gradients multiple corruption estimation noise

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Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Image noise, characterized by random pixel variations, significantly impacts image quality.
Accurate noise parameter estimation is vital for image modeling, denoising, and quality assessment.
Existing methods often struggle to estimate parameters for multiple noise types simultaneously.

Purpose of the Study:

To develop a unified method for estimating parameters of various image noise types.
To address the limitation of single-noise-type estimators.
To enhance the accuracy and applicability of noise parameter estimation.

Main Methods:

Designed a novel noise data feature extractor for effective noise information extraction from image pairs.
Integrated existing single-noise-type estimation algorithms.
Developed the DE-G (Denoising Estimation - General) method.

Main Results:

DE-G accurately estimates additive, multiplicative, and impulsive noise parameters from single-source images.
The method demonstrates capability in estimating parameters for multiple noise corruptions.
Achieved superior performance compared to single-type estimators.

Conclusions:

DE-G provides a robust and accurate solution for estimating multiple image noise types.
This advancement is crucial for improving image denoising and quality assessment.
The proposed method offers a significant step towards comprehensive image noise analysis.