Wavelet Threshold Denoising of Thermal Image from Transmission Joints

Qi, Chao; Wang, Ziyu; Han, Jun; Sai, Qi

doi:10.1109/icm.2011.280

Cited by 5 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AWGN is commonly introduced by the temperature of the sensor and the level of illumination in the environment that corrupts every pixels, whereas IN is caused by faulty sensor triggers or transmissions corrupting a certain image pixels [2]. Due to the common origin, these noises often occur simultaneously in practice which includes digital photography [3], tomography [4] and thermal imaging [5]. This mixed noise removal has been studied fairly well in the past [6]- [13].…”

Section: Introductionmentioning

confidence: 99%

A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images

Islam

Saha

Rahman

et al. 2018

2018 10th International Conference on Electrical and Computer Engineering (ICECE)

View full text Add to dashboard Cite

Reduction of mixed noise is an ill posed problem for the occurrence of contrasting distributions of noise in the image. The mixed noise that is usually encountered is the simultaneous presence of additive white Gaussian noise (AWGN) and impulse noise (IN). A standard approach to denoise an image with such corruption is to apply a rank order filter (ROF) followed by an efficient linear filter to remove the residual noise. However, ROF cannot completely remove the heavy tail of the noise distribution originating from the IN and thus the denoising performance can be suboptimal. In this paper, we present a variational step to remove the heavy tail of the noise distribution. Through experiments, it is shown that this approach can significantly improve the denoising performance of mixed AWGN-IN using well-established methods.Index Terms-l 1 norm, mixed noise removal, variational approach

show abstract

Section: Introductionmentioning

confidence: 99%

A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images

Islam

Saha

Rahman

et al. 2018

2018 10th International Conference on Electrical and Computer Engineering (ICECE)

View full text Add to dashboard Cite

show abstract

Early warning of current‐carrying faults based on deterioration assessment of electric contacts

Zhang

2018

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

This article presents a novel early warning approach of current‐carrying faults of electric equipment based on the deterioration assessment of electric contacts. We propose to detect the temperature diversity among three phases at a certain site by employing the singular entropy analysis to the temperature sequences. Then, power spectrum density is employed to analyze the deterioration and to locate the potential faulty contacts while accomplishing the precaution task. Experimental results are presented to verify the feasibility and validity of the proposed method. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

show abstract

Optical flow-based motion-blurred image restoration for transmission line icing airborne monitoring

Xiao

Jia

et al. 2018

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

View full text Add to dashboard Cite

Wavelet Threshold Denoising of Thermal Image from Transmission Joints

Cited by 5 publications

References 6 publications

A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images

A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images

Early warning of current‐carrying faults based on deterioration assessment of electric contacts

Optical flow-based motion-blurred image restoration for transmission line icing airborne monitoring

Contact Info

Product

Resources

About