Content-Aware Sampling Region Synthesis for Image Hole Filling
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Solution Overview
Problem
Conventional content-aware fill tools often produce unnatural and inaccurate results when filling holes in images, as they may fill the hole with the wrong image content, leading to inconsistent and disjointed textures or colors.
Innovation Solution
The technique involves synthesizing a content-aware sampling region by identifying a band of pixels surrounding the hole, clustering these pixels based on characteristics like color and depth, and using a segmentation algorithm to form a sampling region that is then used to generate a more accurate fill.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional randomized algorithms are used to identify nearest neighbor matches for hole filling, then the process is simple and fast, but the fill results are inaccurate and unnatural
Solution Approach 1:
The sampling region synthesis process is segmented into multiple distinct stages: identifying a band of context pixels surrounding the hole, clustering these pixels based on characteristics like color and depth, passing clusters to a segmentation algorithm, and unioning results to form the final sampling region. This segmentation allows each stage to be optimized independently, improving fill accuracy without overwhelming complexity.
Solution Approach 2:
The patent performs preliminary actions by first identifying and clustering context pixels before the actual hole filling process. The sampling region is synthesized in advance with semantic understanding of the image content, ensuring that only relevant and semantically similar patches are considered for filling, thereby improving accuracy before the main filling operation begins.
2Reliability
If a broad sampling region is used for content-aware fill, then more patch options are available, but irrelevant or mismatched content may be included in the fill
Solution Approach 1:
The patent applies local quality by making the sampling region adaptive to local image characteristics. Different regions around the hole are clustered based on their specific characteristics (color, depth, focus), and the sampling region is constructed to include only areas with semantically similar content. This ensures that the sampling region is flexible and adapts to the local context, improving reliability without sacrificing versatility.
Solution Approach 2:
The patent changes parameters by using multiple clustering characteristics (color, depth, focus, texture) to define the sampling region. By varying these parameters and their weights, the system can adapt the sampling region to different image types and hole locations, ensuring high reliability while maintaining flexibility across diverse scenarios.
3Measurement precision
If manual region selection is required for accurate filling, then fill quality improves, but user time and effort increase
Solution Approach 1:
The system performs self-service by automatically synthesizing the sampling region without requiring manual user input. The algorithm independently identifies context pixels, clusters them based on multiple characteristics, and constructs the sampling region autonomously. This self-service capability achieves high fill precision while eliminating the time loss associated with manual region selection, as the system serves itself in defining the optimal sampling area.
Solution Approach 2:
The patent replaces the mechanical manual selection process with an automated computational system. Instead of requiring users to manually draw or select regions, the system uses algorithms to automatically identify, cluster, and synthesize the sampling region based on image characteristics. This substitution maintains high precision while dramatically reducing the time and effort required from the user.
Data Source
AI summary
Embodiments of the present invention provide systems, methods, and computer storage media for automatically synthesizing a content-aware sampling region for a hole-filling algorithm such as content-aware fill. Given a source image and a hole (or other target region to fill), a sampling region can be synthesized by identifying a band of pixels surrounding the hole, clustering these pixels based on one or more characteristics (e.g., color, x/y coordinates, depth, focus, etc.), passing each of the resulting clusters as foreground pixels to a segmentation algorithm, and unioning the resulting pixels to form the sampling region. The sampling region can be stored in a constraint mask and passed to a hole-filling algorithm such as content-aware fill to synthesize a fill for the hole (or other target region) from patches sampled from the synthesized sampling region.