Imaging Process Initialization Using Directional Color Estimates
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Solution Overview
Problem
Conventional image editing techniques, such as hole filling and image healing, rely on inaccurate color estimates due to the use of nearest neighbor pixels from different semantic regions, leading to unrealistic results.
Innovation Solution
The proposed solution involves generating color estimates for pixels within a region by averaging or weighting pixels outside the region, using structured initialization techniques that consider pixels at opposing directions, such as horizontal and vertical directions, to improve the accuracy of color estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If nearest neighbor technique is used to initialize pixels, then the initialization process is simple and fast, but the color estimate accuracy deteriorates leading to unrealistic results
Solution Approach 1:
The patent segments the single nearest neighbor pixel selection into multiple directional samples (e.g., horizontal, vertical, diagonal directions). Instead of relying on one pixel, the method divides the sampling process into multiple directional components, each contributing to the final color estimate. This segmentation allows the system to capture diverse semantic information from different directions while maintaining computational efficiency.
Solution Approach 2:
The patent merges multiple color estimates from different directional samples into a unified color estimate for the target pixel. By combining information from horizontal, vertical, and diagonal directions, the method creates a more accurate and semantically appropriate color estimate than any single direction could provide alone. This merging process resolves the contradiction by aggregating multiple simple samples into a refined result.
2Device complexity
If single nearest neighbor pixel is used for color estimation, then computational complexity is low, but semantic region consistency deteriorates causing artifacts
Solution Approach 1:
The patent segments the pixel selection process into multiple directional samples rather than relying on a single nearest neighbor. By dividing the sampling into horizontal, vertical, and diagonal components, the method ensures that at least some samples will come from semantically consistent regions, reducing artifacts while keeping computational complexity manageable through structured sampling.
Solution Approach 2:
The patent incorporates a weighting mechanism that provides feedback based on the quality and reliability of each directional sample. Samples that better represent the semantic region receive higher weights, while less reliable samples are downweighted. This feedback loop ensures semantic region consistency without requiring complex computational processes, as the weighting adapts based on the actual image content.
3Measurement precision
If multiple pixels are sampled from different directions, then color estimate accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the sampling process into a fixed, small number of directional samples (e.g., 4-8 directions) rather than sampling all possible pixels. This segmentation limits the processing time by pre-defining the sampling structure, while still achieving improved accuracy through multi-directional coverage. The segmented approach balances accuracy improvement with time constraints.
Solution Approach 2:
The patent applies partial sampling by selecting only the most relevant directional samples rather than processing all possible pixels. By sampling from key directions (horizontal, vertical, diagonal) and applying weighting to the most informative samples, the method achieves sufficient accuracy without the excessive processing time that would result from comprehensive sampling of all surrounding pixels.
Data Source
AI summary
Imaging process initialization techniques are described. In an implementation, a color estimate is generated for a plurality of pixels within a region of an image. A plurality of pixels outside of the regions are first identified for each pixel of the plurality of pixels within the region. This may include identification of pixels disposed at opposing directions from the pixel being estimated. A color estimate is determined for each of the plurality of pixels based on the identified pixels. As part of this, a weighting may be employed, such as based on a respective distance of each of the pixels outside of the region to the pixel within the region, a distance along the opposing direction for corresponding pixels outside of the region (e.g., at horizontal or vertical directions), and so forth. The color estimate is then used to initialize an imaging process technique.