TY - JOUR ID - 144810 TI - Reconstruction of illegible QR codes using deep neural network JO - Journal of Machine Vision and Image Processing JA - JMVIP LA - en SN - AU - monfared, milad AU - koochari, abbas AD - MSc. student in Artificial Intelligence, Department of Mechanic, Electrical and Computer Science, Islamic Azad University Science and Research, Tehran, Iran AD - Department of Mechanic, Electrical and Computer Science, Islamic Azad University Science and Research, Tehran, Iran Y1 - 2022 PY - 2022 VL - 9 IS - 3 SP - 79 EP - 89 KW - Noise Removal KW - QR code reconstruction KW - auto-encoder neural network KW - MCNN KW - Deep Learning KW - Machine Learning DO - N2 - Todays, barcodes play a significant role in various industries, and among the two-dimensional barcodes, the most famous one is QR code (Quick Response code) that has grown widely.The main purpose of this paper is to provide a noise-cancellation method based on a autoencoder deep neural network that can be used to restore distorted and illegible QRs to readability.To create noise and distortion, unlike other articles that used added simulated noise to the image, the challenge of extracting QR coded into a color image was used to collect more realistic data by collecting real-world dataset. therefore we Have more reliable estimation of proposed QRs noise-canceling method. As a result, we created a comprehensive data set of distorted QRs from three different watermark extraction approaches after the screen-camera attack. For the noise reduction process, three independent MCNN networks ( which is an upgrade from the U-net network) are used for each of the three extraction approaches, UR - https://jmvip.sinaweb.net/article_144810.html L1 - https://jmvip.sinaweb.net/article_144810_df9bf0022b382c38383fc3601e2d3874.pdf ER -