BlurPaint: Image Inpainting using Blurring Diffusion Models

Abstract

The denoising diffusion probabilistic model (DDPM) for image inpainting transforms the input image to Gaussian noise by adding noise in the forward process, and reconstitution masked regions that are consistent with unmasked regions from pure noise in the reverse process. Recently, on inpainting methods based on DDPM, the unmasked regions failed to completely degrade into the same pure noise as the masked regions in the forward process, while in the reverse process, the unmasked and masked regions began to denoise from the pure noise at the same time, which led to the inconsistency between the forward and reverse processes. To this end, we propose BlurPaint, which completely degrades unmasked regions in the forward process and keeps consistent with masked regions that become Gaussian noise. In the reverse process, the unmasked regions are combined to complete the image. To better extract useful information from pure noise, a multi-scale feature fusion module (MFFM) is designed as the component of the denoise network, which learns local and non-local features through multi-scale receptive fields. Experimental results on multiple benchmark datasets demonstrate the performance of the proposed model. Our code is available at https://github.com/vpsg-research/BlurPaint.