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Research Article | Open Access
Volume 15 2023 | None
FACE RESTORING
Dr. S. SOWJANYA, Bachupally Vikhitha, Punnam Varshini, Surepally Pavan Kumar, ALLU HRUDAI RAJU
Pages: 796-801
Abstract
Blind face restoration aims at recovering high-quality faces from the low-quality counterparts suffering from unknown degradation, such as low-resolution, noise, blur, compression artefacts etc. When applied to real-world scenarios, it becomes more challenging, due to more complicated degradation, diverse poses and expressions. We use GFP-GAN i.e., a generative adversarial network for blind face restoration that leverages a generative facial prior (GFP). This Generative Facial Prior (GFP) is incorporated into the face restoration process via channel-split spatial feature transform layers, which allow for a good balance between realness and fidelity. This project involves Deep learning model in order to raise the quality of picture. It is advantages with restoring any kind of pictures. This project basically focuses at one specific neural network architecture that can take blurry, and distorted photos of human faces and restore them into near-perfect, realistic images. Several neural network architectures can be used to achieve this – we will look at two of them specifically – GFP-GAN.
Keywords
Blind face restoration aims at recovering high-quality faces from the low-quality counterparts suffering from unknown degradation, such as low-resolution, noise, blur, compression artefacts etc.
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