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• W4387068400 abstract "In the twenty-first century, deep learning and artificial intelligence are being used to solve multiple problems in the real world. When coming to images, removing certain unwanted objects from images as per our interest has been one of the most famous intriguing problem statements in photo-editing. Image inpainting is a technique which is used to fill the missing regions present in images is a promising yet difficult activity. Previous approaches have accomplished critical advancement using GANs. However, these methodologies might experience the ill effects of reproducing distorted images and blurred textures in high-resolution pictures. The difficulties primarily come from content understanding from fine-grained and distant contexts image content understanding in a missing region in an image. Being an avid researcher and enthusiast in the field of artificial intelligence, working in such a problem statement would be a good way to hone the skills in the field. We propose an enhanced model using GANs for image inpainting of high-resolution images. The enhanced block helps in capturing both rich patterns as well as context from distant regions. The discriminator is also tweaked to make sure the robustness of the model." @default.
• W4387068400 created "2023-09-27" @default.
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• W4387068400 date "2023-01-01" @default.
• W4387068400 modified "2023-09-27" @default.
• W4387068400 title "High-Resolution Image Inpainting Using Generative Adversarial Networks" @default.
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• W4387068400 doi "https://doi.org/10.1007/978-981-99-2742-5_73" @default.
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