Image Inpainting via Structural Feature Extraction
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Solution Overview
Problem
Existing image inpainting techniques often result in noise such as blurring or distortion, especially when dealing with images that have damaged or empty regions with varying shapes, positions, and sizes.
Innovation Solution
An electronic device performs image inpainting by determining missing regions in an original image, generating an input image, obtaining a mask image, and extracting structural features using a first model. These features are then used to reconstruct the missing region in the input image using a second model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image inpainting techniques are used to fill damaged or empty regions, then the missing regions can be reconstructed, but noise such as blurring or distortion occurs in the inpainted image
Solution Approach 1:
The patent segments the inpainting process into multiple stages: generating an initial inpainted image, detecting structural features (lines and curves) in both the original and inpainted images, and selectively adjusting regions where structural features differ. This segmentation allows precise control over which areas are refined, reducing overall noise while maintaining reconstruction capability.
Solution Approach 2:
The patent applies local quality adjustment by identifying specific regions where structural features differ between the original and inpainted images, and selectively refining only those regions. This ensures that noise reduction is applied locally where needed rather than uniformly across the entire image, preserving sharp edges and structural integrity in critical areas.
2Adaptability or versatility
If the missing region is large or has complex shape, then the reconstruction challenge increases, but conventional techniques produce more noticeable artifacts and distortion
Solution Approach 1:
The patent employs dynamic adjustment by detecting structural features in both the original and inpainted images and comparing them to determine which regions require refinement. This dynamic approach adapts to different missing region sizes and shapes by selectively applying refinement only where structural discrepancies exist, rather than using a fixed threshold or uniform processing approach.
Solution Approach 2:
The patent implements feedback by detecting structural features (lines and curves) in both the original image and the inpainted image, comparing these features to identify regions where the inpainting deviated from the original structure, and then selectively refining those regions. This feedback loop ensures that the refinement process targets only areas needing improvement, maintaining high quality across various missing region configurations.
Data Source
AI summary
An image inpainting method is provided. The image inpainting method includes determining a missing region in an original image, generating an input image to be reconstructed from the original image, based on the missing region, obtaining a mask image indicating the missing region, determining whether to extract a structural feature of the missing region, based on an attribute of the missing region, obtaining structure vectors each consisting of one or more lines and one or more junctions by applying the input image and the mask image to a first model for extracting a structural feature of the input image, and obtaining an inpainted image in which the missing region in the input image is reconstructed by applying the input image, the mask image, and a structure vector image converted from the structure vectors to a second model for reconstructing the input image.