20 research outputs found
Generative Modeling in Structural-Hankel Domain for Color Image Inpainting
In recent years, some researchers focused on using a single image to obtain a
large number of samples through multi-scale features. This study intends to a
brand-new idea that requires only ten or even fewer samples to construct the
low-rank structural-Hankel matrices-assisted score-based generative model
(SHGM) for color image inpainting task. During the prior learning process, a
certain amount of internal-middle patches are firstly extracted from several
images and then the structural-Hankel matrices are constructed from these
patches. To better apply the score-based generative model to learn the internal
statistical distribution within patches, the large-scale Hankel matrices are
finally folded into the higher dimensional tensors for prior learning. During
the iterative inpainting process, SHGM views the inpainting problem as a
conditional generation procedure in low-rank environment. As a result, the
intermediate restored image is acquired by alternatively performing the
stochastic differential equation solver, alternating direction method of
multipliers, and data consistency steps. Experimental results demonstrated the
remarkable performance and diversity of SHGM.Comment: 11 pages, 10 figure