Learning Sparse Masks for Diffusion-based Image Inpainting

Autor:	Alt, Tobias, Peter, Pascal, Weickert, Joachim
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse data. Its quality strongly depends on the choice of known data. Optimising their spatial location -- the inpainting mask -- is challenging. A commonly used tool for this task are stochastic optimisation strategies. However, they are slow as they compute multiple inpainting results. We provide a remedy in terms of a learned mask generation model. By emulating the complete inpainting pipeline with two networks for mask generation and neural surrogate inpainting, we obtain a model for highly efficient adaptive mask generation. Experiments indicate that our model can achieve competitive quality with an acceleration by as much as four orders of magnitude. Our findings serve as a basis for making diffusion-based inpainting more attractive for applications such as image compression, where fast encoding is highly desirable. Comment: To appear in A. J. Pinho, P. Georgieva, L. F. Teixeira, J. A. S\'anchez (Eds.): Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, Springer, Cham, 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2110.02636 Zobrazit plný text záznamu View this record from Arxiv