Seamless Image Composition via Gradient Matching
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Solution Overview
Problem
Creating seamless image compositions from images with drastically different characteristics, such as color saturation, contrast, and exposure levels, often results in poor quality near the boundaries, especially when combining daytime and nighttime images or high-exposed, medium-exposed, and low-exposed images.
Innovation Solution
A method for blending multiple images into a composite by matching image gradients across seams, using blend parameters to adjust the weighting of regions from different images, and iteratively refining the blending process based on user feedback to minimize edge sharpness and blurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If images with drastically different characteristics (color saturation, contrast, exposure levels) are blended together, then the composite image can incorporate diverse visual information, but the quality near boundaries deteriorates with visible seams and artifacts
Solution Approach 1:
The patent applies local quality by computing and applying different blend parameters to different regions of the image, specifically using gradient-based blending at boundaries versus simpler blending in interior regions. This allows the system to handle diverse image characteristics globally while maintaining high boundary quality locally through region-specific processing.
Solution Approach 2:
The patent changes blending parameters dynamically based on local image characteristics. It computes gradients at boundaries and uses these to determine blend amounts, transitioning from fixed blend parameters to adaptive ones that vary spatially. This resolves the contradiction by allowing versatile blending of different image types while maintaining precision at boundaries through parameter adaptation.
2Manufacturing precision
If gradient matching is applied across seams to improve boundary quality, then seamless transitions are achieved, but computational complexity increases
Solution Approach 1:
The patent segments the blending process into distinct regions: interior regions using simple blend parameter application, and boundary regions requiring gradient computation and matching. This segmentation reduces overall computational complexity by applying complex gradient-based methods only where necessary at seams, rather than across the entire image.
Solution Approach 2:
The patent applies gradient matching locally at boundaries rather than globally across the entire image. By computing gradients only at seam regions and applying localized corrections, the system achieves seamless transitions at boundaries while avoiding the excessive computational cost of global gradient operations.
3Manufacturing precision
If blend parameters are adjusted to minimize edge sharpness and blurring, then visual coherence improves, but the number of iterative refinements required increases
Solution Approach 1:
The patent performs preliminary computation of gradients and blend parameters before the actual blending operation. By pre-computing the necessary parameters based on image characteristics and boundary locations, the system reduces the number of iterative refinements needed during the blending process, thereby reducing time loss while maintaining visual coherence.
Solution Approach 2:
The patent incorporates feedback mechanisms where blend parameters are initially set, blending is performed, boundary quality is evaluated, and parameters are refined based on the evaluation. This iterative feedback process converges efficiently by using gradient information to guide parameter adjustments, achieving visual coherence with minimal iterations.
Data Source
AI summary
Seamless image compositions may be created. A plurality of images may be received, one image being a base image and the remaining image or images being source images. A selection may be received of one or more regions of one or more of the sources images to be copied onto the base image. An input may be received setting a blend parameter for each of the selected regions. The plurality of images may be blended together into a composite image by matching image gradients across one or more seams of the composite image. The image gradients may be based on the blend parameters. In one embodiment, input may be received to set a modified blend parameter for at least one of the selected regions and the plurality of images may be re-blended into a composite image in a similar manner but using the modified blend parameter.