Gradient-Domain Compositing for Seamless Image Stitching
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Solution Overview
Problem
Existing methods for forming composite images often result in visible seams and artifacts due to variations in tone, size, and texture between overlapping images, which can be difficult to align and merge without compromising image quality.
Innovation Solution
The use of gradient-domain compositing with a quadtree data structure to calculate and interpolate pixel values along seams, reducing the number of processed pixels and minimizing artifacts by blending seams between aligned source images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional stitching methods are used to merge overlapping images, then the composite image can be formed, but visible seams and artifacts appear due to variations in tone, size, and texture between images
Solution Approach 1:
The patent introduces gradient domain as an intermediary representation between the original image space and the final composite image. By working in the gradient domain (using Poisson equations to solve for pixel values based on gradients), the method mediates the blending process to smoothly transition between overlapping images, eliminating visible seams and artifacts while maintaining image quality.
2Manufacturing precision
If all pixels in the assembled composite image are processed for stitching, then seamless blending is achieved, but computational load and processing time increase significantly
Solution Approach 1:
The patent segments the image processing task by identifying and prioritizing seam regions where blending is critical, while using interpolation for smooth regions. This segmentation allows the computationally intensive gradient domain compositing to be applied only where necessary (at seams), while other regions are processed more efficiently through interpolation, significantly reducing overall computational load and processing time.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image: high-quality gradient domain compositing is applied specifically to seam regions where blending is needed, while smooth regions use simpler interpolation methods. This local quality approach ensures seamless blending at critical areas without unnecessarily processing the entire image at maximum quality, optimizing the balance between blending quality and processing efficiency.
3Manufacturing precision
If multiple overlapping images are captured to create high-resolution composite images, then the final image contains more pixels and higher resolution, but aligning and merging the images becomes more complex
Solution Approach 1:
The patent uses gradient domain as an intermediary mathematical framework that simplifies the complex alignment and merging process. By formulating the blending problem as a Poisson equation in the gradient domain, the method provides a systematic approach to handling multiple overlapping images with different orientations and scales, making the merging process more manageable while maintaining high resolution.
Data Source
AI summary
Systems, methods, and apparatus, including computer program products, for forming composite images using gradient-domain compositing are provided. In some implementations, a method is provided. The method includes receiving two or more source images and aligning the received source images to form an assembled composite image. The method also includes stitching the seams between the aligned source images in the assembled composite image to form a final composite image. The stitching includes performing a gradient domain compositing. The gradient domain compositing uses a subset of pixels in the assembled composite image including calculating individual pixel values along the seams and interpolating pixel values away from the seams.