Rejuvenate: Face and Body Retouching Using Image Inpainting
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Rejuvenate: Face and Body Retouching Using Image Inpainting

Authors: H. AbdelRahman, S. Rostom, Y. Lotfy, S. Salah Eldeen, R. Yassein, N. Awny

Abstract:

People are growing more concerned with their appearance in today's society. But they are terrified of what they will look like after a plastic surgery. People's mental health suffers when they have accidents, burns, or genetic issues that cause them to cleave certain body parts, which makes them feel uncomfortable and unappreciated. The method provides an innovative deep learning-based technique for image inpainting that analyzes different picture structures and fixes damaged images. This study proposes a model based on the Stable Diffusion Inpainting method for in-painting medical images. One significant advancement made possible by deep neural networks is image inpainting, which is the process of reconstructing damaged and missing portions of an image. The patient can see the outcome more easily since the system uses the user's input of an image to identify a problem. It then modifies the image and outputs a fixed image.

Keywords: Generative Adversarial Network, GAN, Large Mask Inpainting, LAMA, Stable Diffusion Inpainting.

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References:


[1] Shuang Chen, Amir Atapour-Abarghouei, Jane Kerby, et al. “A Feasibility Study on Image Inpainting for Non- cleft Lip Generation from Patients with Cleft Lip”. In: arXiv preprint arXiv:2208.01149 (2022).
[2] Robin Rombach, Andreas Blattmann, Dominik Lorenz, et al. “High-resolution image synthesis with latent dif- fusion models. 2022 IEEE”. In: CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2022, pp. 10674–10685.
[3] Ilkin Sevgi Isler, Chase Walker, Dominic Simon, et al. “Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding”.
[4] Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, et al. “Resolution-robust large mask inpainting with fourier convolutions”. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2022, pp. 2149–2159.
[5] Youngjoo Jo and Jongyoul Park. “Sc-fegan: Face editing generative adversarial network with user’s sketch and color”. In: Proceedings of the IEEE/CVF international conference on computer vision. 2019, pp. 1745–1753.
[6] Karim Armanious, Youssef Mecky, Sergios Gatidis, et al. “Adversarial inpainting of medical image modalities”. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2019, pp. 3267–3271.
[7] Robin Rombach, Andreas Blattmann, Dominik Lorenz, et al. “High-Resolution Image Synthesis with Latent Diffusion Models”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 2022, pp. 10684–10695.
[8] SVM Vishwanathan and M Narasimha Murty. “SSVM: a simple SVM algorithm”. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No. 02CH37290). Vol. 3. IEEE. 2002, pp. 2393–2398.
[9] Alain Hore and Djemel Ziou. “Image quality metrics: PSNR vs. SSIM”. In: 2010 20th international conference on pattern recognition. IEEE. 2010, pp. 2366–2369.