Towards Seamless Borders: A Method for Mitigating Inconsistencies in Image Inpainting and Outpainting
This work addresses a specific challenge in image inpainting for computer vision applications, representing an incremental improvement.
The paper tackled the problem of achieving seamless continuity in image inpainting by addressing discrepancy issues in diffusion-based models, resulting in methods that effectively reduce discontinuity and produce high-quality, coherent inpainting results.
Image inpainting is the task of reconstructing missing or damaged parts of an image in a way that seamlessly blends with the surrounding content. With the advent of advanced generative models, especially diffusion models and generative adversarial networks, inpainting has achieved remarkable improvements in visual quality and coherence. However, achieving seamless continuity remains a significant challenge. In this work, we propose two novel methods to address discrepancy issues in diffusion-based inpainting models. First, we introduce a modified Variational Autoencoder that corrects color imbalances, ensuring that the final inpainted results are free of color mismatches. Second, we propose a two-step training strategy that improves the blending of generated and existing image content during the diffusion process. Through extensive experiments, we demonstrate that our methods effectively reduce discontinuity and produce high-quality inpainting results that are coherent and visually appealing.