Image Inpainting via Object-Background Scratch Segmentation
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Solution Overview
Problem
Existing image inpainting methods struggle to effectively restore images with scratches and local damage, especially for non-repeating patterns, resulting in poor inpainting effects.
Innovation Solution
The proposed method involves determining object and background scratch regions using a segmentation network model and U-Net, followed by image inpainting using specific network models for each region, and finally fusing the inpainted object and background images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image inpainting methods are used, then the processing speed is maintained, but the inpainting effect is poor especially for non-repeating patterns
Solution Approach 1:
The scratch region is segmented into object scratch region and background scratch region based on object region detection. This segmentation allows different inpainting strategies to be applied to different regions, improving the overall inpainting effect while managing complexity through modular processing
Solution Approach 2:
Different network models are used for different regions: a first network model specifically trained for object regions handles object scratch regions, while a second network model handles background scratch regions. This local differentiation improves inpainting quality for non-repeating patterns without requiring a single overly complex model
2Manufacturing precision
If a single network model is used for inpainting, then the device complexity is reduced, but the inpainting precision for different regions deteriorates
Solution Approach 1:
The scratch region is divided into object scratch region and background scratch region, enabling the use of specialized network models for each region type. This segmentation justifies the increased model complexity by delivering superior inpainting precision for different regional characteristics
Solution Approach 2:
The first network model is specifically trained on object images to handle object scratch regions, while the second network model handles background regions. This localized specialization improves precision without requiring a single monolithic complex model, as each sub-model can be optimized for its specific region type
Data Source
AI summary
Embodiments of the present disclosure relate to the technical field of image processing, and provide an image inpainting method and device. The method includes: obtaining an image to be inpainted; determining an object area and a scratch area of the image to be inpainted, wherein the object area is an area where a target object in the image to be inpainted is located; determining an object scratch area and a background scratch area according to the object area and the scratch area; performing image inpainting on the object area according to the object scratch area to obtain an object image; performing image inpainting on the image, to be inpainted, according to the background scratch area to obtain a background image; and fusing the object image and the background image to obtain an inpainted image of the image to be inpainted.