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Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes

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Published on: May 23, 2017

Framelet algorithms for de-blurring images corrupted by impulse plus Gaussian noise.

Yan-Ran Li¹, Lixin Shen, Dao-Qing Dai

¹Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. lyran@szu.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 11, 2011

Summary

This study introduces a new image restoration algorithm (IFASDA) for images with Gaussian and impulse noise. The parameter-free IFASDA algorithm improves image quality and peak signal-to-noise ratio (PSNR) over existing methods.

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Area of Science:

Image Processing
Computer Vision
Applied Mathematics

Background:

Observed images are frequently degraded by combined Gaussian and impulse noise.
Existing image restoration methods often rely on minimizing objective functionals with l(1) fidelity and Mumford-Shah regularization.

Purpose of the Study:

To develop a novel image restoration algorithm for images corrupted by both Gaussian and impulse noise.
To introduce a new objective functional with a content-dependent fidelity term and a tight framelet-based regularizer.

Main Methods:

A new objective functional is proposed, incorporating content-dependent fidelity (l(1) and l(2) norms) and a tight framelet regularizer.
An iterative framelet-based approximation/sparsity deblurring algorithm (IFASDA) is developed for the proposed functional.
Parameters in IFASDA are adaptively varied, making it a parameter-free algorithm.

Main Results:

The proposed IFASDA algorithm demonstrates effectiveness in restoring images corrupted by Gaussian and impulse noise.
Experimental results show improvements in both Peak Signal-to-Noise Ratio (PSNR) and visual quality compared to existing methods.
An accelerated version, Fast_IFASDA, is also developed.

Conclusions:

The developed IFASDA algorithm offers a practical and effective solution for image restoration problems with mixed noise.
The parameter-free nature and improved performance make IFASDA a valuable contribution to image restoration techniques.