Image Inpainting via Warped Source Merging for Parallax Resolution
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Solution Overview
Problem
Existing image inpainting technologies face inaccuracies when dealing with large, irregular holes, parallax issues, and color mismatching, particularly in high-resolution images captured from different angles or exposure levels, leading to poor quality output.
Innovation Solution
The method involves generating and merging warped copies of a source image using neural network models to align and adjust pixels, ensuring accurate inpainting by matching the target image's perspective, color, and exposure levels, even for complex occlusions and parallax scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image inpainting technologies are used, then the process is simple, but the accuracy deteriorates when dealing with large holes, irregular shapes, and parallax effects
Solution Approach 1:
The patent divides the inpainting process into multiple stages: generating multiple warped copies of the source image with different transformations, selecting appropriate warped copies based on similarity to the target image, and merging selected warped copies to create the final inpainted region. This segmentation allows each stage to focus on specific aspects of accuracy without overwhelming complexity.
Solution Approach 2:
The patent introduces a new dimension by generating multiple warped copies of the source image with different transformation parameters (perspective, rotation, scaling) and then selecting from these multi-dimensional variations. This transforms the single-image inpainting problem into a multi-hypothesis problem, significantly improving accuracy for complex scenarios like large holes and parallax effects.
2Measurement precision
If single image inpainting is used, then the process is fast, but color mismatching and exposure level differences occur
Solution Approach 1:
The patent performs preliminary transformations on the source image to generate multiple warped copies with different perspectives and properties before the actual inpainting process. This preliminary action allows the system to pre-compute various possible matches, reducing the time needed during the selection and merging stages while achieving better color and exposure matching.
Solution Approach 2:
The patent changes parameters such as perspective transformation, rotation angles, and scaling factors to generate multiple warped copies of the source image. By varying these parameters, the system can find transformations that better match the target image's color and exposure characteristics, improving matching accuracy without requiring excessive processing time.
3Measurement precision
If multiple warped copies are generated and merged, then inpainting accuracy improves, but computational complexity increases
Solution Approach 1:
The patent generates multiple warped copies (more than strictly necessary) and then selects only the most relevant ones based on similarity metrics. This partial action approach ensures that sufficient warped copies are generated to achieve high accuracy, while the selection process filters out unnecessary computations, balancing accuracy improvement with computational efficiency.
Data Source
AI summary
Various disclosed embodiments are directed to inpainting one or more portions of a target image based on merging (or selecting) one or more portions of a warped image with (or from) one or more portions of an inpainting candidate (e.g., via a learning model). This, among other functionality described herein, resolves the inaccuracies of existing image inpainting technologies.