SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting

Recently, deep learning has enabled a huge leap forward in image inpainting. However, due to the memory and computational limitation, most existing methods are able handle only low-resolution inputs, typically less than 1 K. With improvement of Internet transmission capacity mobile device cameras, resolution video sources available users via cloud or locally is increasing. For high-resolution images, common inpainting simply upsample inpainted result shrinked yield blurry result. In recent years, there an urgent need reconstruct missing high-frequency information images generate sharp texture details. Hence, we propose general framework for inpainting, which first hallucinates semantically continuous blurred using suppresses overhead. Then details with original reconstructed super-resolution refinement. Experimentally, our method achieves inspiring quality on 2K 4K ahead state-of-the-art technique. This expected be popularized editing tasks personal computers devices future.