Improving Detail in Pluralistic Image Inpainting with Feature Dequantization

Kyungri Park, Woohwan Jung

Abstract:

Pluralistic Image Inpainting (PII) offers multiple plausible solutions for restoring missing parts of images and has
been successfully applied to various applications including image editing and object removal. Recently, VQGANbased methods have been proposed and have shown that
they significantly improve the structural integrity in the generated images. Nevertheless, the state-of-the-art VQGANbased model PUT faces a critical challenge: degradation
of detail quality in output images due to feature quantization. Feature quantization restricts the latent space and
causes information loss, which negatively affects the detail
quality essential for image inpainting. To tackle the problem, we propose the FDM (Feature Dequantization Module) specifically designed to restore the detail quality of images by compensating for the information loss. Furthermore, we develop an efficient training method for FDM
which drastically reduces training costs. We empirically
demonstrate that our method significantly enhances the detail quality of the generated images with negligible training and inference overheads. The code is available at
https://github.com/hyudsl/FDM