Image Inpainting Feature Adjustment for Texture Consistency
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Solution Overview
Problem
Existing image inpainting methods fail to consider the constraint relationship between known partial images and predicted results, resulting in inpainted images with poor semantic information and texture consistency.
Innovation Solution
An image processing method that adjusts feature set values for representing unknown regions using statistical information from known regions, enhancing color and texture consistency by directly extracting context features from partial images without introducing unnecessary prior information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing image inpainting methods are used to generate predicted results from partial images, then the inpainting process can be completed, but the semantic information and texture consistency of the inpainted image deteriorates
Solution Approach 1:
The patent applies feedback by using the known partial image to provide constraint information that continuously guides and refines the prediction process. The constraint relationship acts as feedback to correct and improve the predicted results, ensuring that the generated semantic information and texture consistently match the original partial image throughout the inpainting process.
Solution Approach 2:
The patent introduces constraint information as an intermediary element that mediates between the partial image and the predicted results. This constraint information serves as a bridge to transfer the semantic and textural characteristics from the known region to the unknown region, improving both semantic information consistency and texture consistency without direct one-to-one mapping.
2Manufacturing precision
If statistical information from known regions is used to adjust feature sets for unknown regions, then color and texture consistency is improved, but the complexity of the processing increases
Solution Approach 1:
The patent changes parameters by adjusting the feature sets of unknown regions using statistical information from known regions. Specifically, it modifies color and texture parameters through statistical adjustment, which improves manufacturing precision (texture consistency) while managing processing complexity through efficient statistical computation rather than exhaustive processing.
3Reliability
If context features are extracted directly from partial images without prior information, then the inpainting result is more consistent with the original image, but the amount of processing required increases
Solution Approach 1:
The patent extracts context features directly from the partial image without introducing unnecessary prior information. This extraction process isolates and utilizes only the essential contextual information present in the input image, improving image consistency while managing processing efficiency by avoiding redundant computations from external prior information sources.
Data Source
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Figure 2B~2E
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AI summary
This application discloses an image processing method, device, and apparatus, and a storage medium. The method is performed by a computing device, the method including: receiving an input image for predicting a target image; determining a context feature of the input image; determining a first feature set and a second feature set according to the context feature and based on a size of the target image and a location of the input image in the target image, and adjusting the second feature set according to a first feature statistic of the first feature set, to obtain an adjusted second feature set; and generating the target image based on the adjusted second feature set and the first feature set.