A GAN-based Background Noise Removal Method on Infrared Image of Gas-Insulated Transmission Line

In the task of the image processing and analysis, the background noise removal is a important step. In the image background noise removal, there are many methods which is popular for the researchers. For example, the gray threshold methods are commonly taken to remove the noises which have large contrast to the interest objects. However, there are many noises with no variance with the interest objects in the gray level. For these noises, the gray level based noise removal method is totally futile, while the contour feature has its super performance for reducing this sort of noise. For the contour feature based image background removal method, the contour model is the key. This paper proposes a novel method for modeling the contour feature of the interest objects. With this method, a novel background noise which has the same gray level to the background noise is totally removed.

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Research on Salt and Pepper Noise Removal Method Based on Adaptive Fuzzy Median Filter

2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ◽

10.1109/iaeac50856.2021.9390923 ◽

2021 ◽

Author(s):

Ping Luo ◽

Xiaoxiao Zhang ◽

Zheng Chang ◽

Wang Liu

Keyword(s):

Median Filter ◽

Noise Removal ◽

Salt And Pepper Noise ◽

Removal Method ◽

Adaptive Fuzzy ◽

Salt And Pepper

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Single Infrared Image Optical Noise Removal Using a Deep Convolutional Neural Network

IEEE Photonics Journal ◽

10.1109/jphot.2017.2779149 ◽

2018 ◽

Vol 10 (2) ◽

pp. 1-15 ◽

Cited By ~ 4

Author(s):

Xiaodong Kuang ◽

Xiubao Sui ◽

Yuan Liu ◽

Qian Chen ◽

Guohua GU

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Infrared Image ◽

Noise Removal ◽

Deep Convolutional Neural Network ◽

Optical Noise

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Background Noise Removal Technique using Deep Learning Segmentation Network without Segmentation Map

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2705 ◽

2021 ◽

Vol 263 (2) ◽

pp. 4441-4445

Author(s):

Hyunsuk Huh ◽

Seungchul Lee

Keyword(s):

Deep Learning ◽

Background Noise ◽

Ground Truth ◽

Noise Removal ◽

Industrial Manufacturing ◽

Proposed Model ◽

Audio Data ◽

Passive Noise ◽

Removal Technique ◽

Noise Canceling

Audio data acquired at industrial manufacturing sites often include unexpected background noise. Since the performance of data-driven models can be worse by background noise. Therefore, it is important to get rid of unwanted background noise. There are two main techniques for noise canceling in a traditional manner. One is Active Noise Canceling (ANC), which generates an inverted phase of the sound that we want to remove. The other is Passive Noise Canceling (PNC), which physically blocks the noise. However, these methods require large device size and expensive cost. Thus, we propose a deep learning-based noise canceling method. This technique was developed using audio imaging technique and deep learning segmentation network. However, the proposed model only needs the information on whether the audio contains noise or not. In other words, unlike the general segmentation technique, a pixel-wise ground truth segmentation map is not required for this method. We demonstrate to evaluate the separation using pump sound of MIMII dataset, which is open-source dataset.

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