Semantic Graph Image Inpainting for Clearer Boundaries
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Solution Overview
Problem
Existing image inpainting technologies result in residual traces of the original image in processed areas, leading to poor image quality due to insufficient consideration of semantic information during the inpainting process.
Innovation Solution
The method involves acquiring a target semantic graph corresponding to the image and using it to inpaint modified areas, considering richer semantic information to improve image quality by reducing residual traces and enhancing boundary clarity and texture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image inpainting technology is used to process modified areas, then the inpainting operation can be completed, but residual traces of the original image remain and image quality deteriorates
Solution Approach 1:
The patent applies preliminary action by performing semantic segmentation on the image before inpainting to obtain a semantic map that guides the subsequent inpainting process. This pre-processing step categorizes image regions by semantic meaning, enabling the inpainting model to understand contextual relationships and generate more accurate fill content that matches surrounding areas, thereby reducing residual traces and improving overall inpainting quality
Solution Approach 2:
The patent changes the parameter of semantic information utilization by integrating semantic maps into the inpainting process. The semantic map provides additional semantic parameters that guide the diffusion model's denoising process, allowing the model to consider semantic consistency when generating inpainted content. This parameter enhancement enables the model to produce results that better match the semantic structure of the original image, reducing visible traces and improving quality
2Manufacturing precision
If semantic information is fully utilized in the inpainting process, then image quality and boundary clarity improve, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct semantic segments through semantic segmentation. This creates a semantic map that partitions the image into meaningful regions, allowing the inpainting process to operate with enhanced structural guidance. The segmentation enables the model to focus computational resources on boundary regions and semantic transitions, improving boundary clarity while managing computational complexity through targeted processing
Solution Approach 2:
The patent introduces a semantic map as an intermediary element between the input image and the inpainting output. This semantic map serves as a mediator that encodes semantic relationships and structural information, guiding the diffusion model's generation process. The intermediary provides a compact representation of semantic structure that improves boundary clarity without requiring the model to directly analyze complex pixel-level relationships, thus managing computational complexity
Data Source
AI summary
Embodiments of the present disclosure provide an image inpainting method, apparatus and an electronic device. The image inpainting method includes: acquiring a first image which is obtained by processing a target object in an original image; determining a first area to be inpainted in the first image, the first area is at least a partial area of the target object; acquiring a target semantic graph corresponding to the first image; and inpainting the first area based on the target semantic graph to obtain a second image after inpainted. Therefore, the semantic graph of the image to be inpainted which contains richer semantic information is considered, thus, the image can be inpainted based on the richer semantic information. Residual traces of the original image in the inpainted image are reduced, the boundaries of different semantic areas are clear, the textures are richer, and the image is more real.