Image-to-image translation based face photo de-meshing using GANs
Jabbar A. Assam M. Arslan M. Bukhsh M. Amin M.S. Ghadi Y.Y. Innab N. Alajmi M. Orken M. Indira S. Alkahtan H.K.
October 2024Academic Press Inc.
Computer Vision and Image Understanding
2024#247
Most of the existing face photo de-meshing methods have accomplished promising results; there are certain quality problems with these methods like the inpainted regions would appear blurry and unpleasant boundaries becoming visible. Such artifacts cause generated face photos unreal. Therefore, we propose an effective image-to-image translation framework called Face De-meshing Using Generative Adversarial Networks (De-mesh GANs). The De-mesh GANs is a two-stage model: (i) binary mask generating module, is a three convolution layers-based encoder–decoder network architecture that automatically generates a binary mask for the meshed region, and (ii) face photo de-meshing module, is a GANs-based network that eliminates the mesh mask and synthesizes the meshed area. An arrangement of careful losses (reconstruction loss, adversarial loss, and perceptual loss) is used to reassure the better quality of the de-mesh face photos. To facilitate the training of the proposed model, we have designed a dataset of clean/corrupted photo pairs using the CelebA dataset. Qualitative and quantitative evaluations of the De-mesh GANs on real-world corrupted face photo images show better performance than the previously proposed face photo de-meshing models. Furthermore, we also offer the ablation study for performance assessment of the additional network i.e., perceptual network.
Autoencoder , Generative adversarial networks , Image recovery
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College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
School of Computer Science Chenab College of Advance Studies Faisalabad, Pakistan
Department of Engineering Mechanics, Zhejiang University, Hangzhou, 310027, China
School of Software Engineering, East China Normal University, Shanghai, 200050, China
Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, 12555, United Arab Emirates
Department of Computer Science and Information Systems, College of Applied Sciences, Al Maarefa University, Diriyah, 13713, Riyadh, Saudi Arabia
Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
Institute information and computation technology, Abai University, Kazakhstan
Kazakh National Pedagogical university after Abai Department of Informatics and Information of Education, Kazakhstan
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
College of Computer Science and Technology
School of Computer Science Chenab College of Advance Studies Faisalabad
Department of Engineering Mechanics
School of Software Engineering
Department of Computer Science and Software Engineering
Department of Computer Science and Information Systems
Department of Computer Engineering
Institute information and computation technology
Kazakh National Pedagogical university after Abai Department of Informatics and Information of Education
Department of Information Systems
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