Deep Learning Based Image Denoising Model for Electronic Manufacturing Industry using NanuNet
Keywords:
Deep Learning, Artificial Intelligence, Image Denoising, Semiconductor waferAbstract
Recent advancement in technology has led the electronic manufacturing industry to be more efficient, robust and fast. The advent of Artificial Intelligence in various domains of engineering has brought many positive changes in production and testing. Since most of the work is based on electronic machines and apparatus in various fields, it is important to work on challenges in different steps of manufacturing and testing of such electronic equipments. Semiconductor wafers are the core and most important part in gadgets and apparatus thus testing and detection of any kind of defect present in the wafer is cardinal for better efficiency. For that it is customary to deal with the noise problem in PCB wafer images. In this paper we have proposed a deep learning based image denoising method for semiconductor wafer testing and noise removal.
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Amirhosein Ghasemabadi, Muhammad Kamran Janjua et al, CascadedGaze: Efficiency in Global Context Extraction for Image Restoration, Transactions on Machine Learning Research, may2024
Cagatay Isil, Tianyi Gan, F. Onuralp Ardic, Koray Mentesoglu et al. All-optical image denoising using a diffractive visual processor, Light: Science & Applications, 2024
Wencong Wu, Shijie Liu et al. Dual Residual Attention Network for Image Denoising, may 2023
Chi-Mao-Fan, Kuan-Hsien Liu et al, Selective Residual M-Net for Real Image Denoising, EUSIPCO, August 2022
Shen Cheng, Yuzhi Wang et al. NBNet: Noise Basis Learning for Image Denoising with Subspace Projection, CVPR 2021
Saeed Anvwar, Nick Barnes, Real Image Denoising with Feature Attention, ICCV, 2019
Arnel C. Fajardo et al, Defect Detection and Classification in Printed Circuit Boards using Convolutional Neural Networks, ICECAA, 2023
Taha Hussain, Hogar K.Omar et al, A study on image noise and various image denoising techniques, ResearchJet journal of analysis and inventions, November 2021
PSNR , https://ieeexplore.ieee.org/document/5596999
SSIM, https://ieeexplore.ieee.org/document/5596999
Shi Guo et al, Toward Convolutional Blind Denoising of Real Photographs, CVPR, 2019
Yiyun Zhao et al, Pyramid Real Image Denoising Network, IEEE, 2019
Saeed Anwar et al, Real Image Denoising with Feature Attention, ICCVW, 2019
junhai zhai, sufang zhang et al, Autoencoder and its Various Varients, IEEE, jan 2019
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