Image Sub-Image Stitching via Edge-Detected Path Blending
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Solution Overview
Problem
Printing oversized images, such as wallpapers, large commercial banners, and panoramic photos, poses challenges as existing methods result in visible stitching lines between sub-images due to inconsistencies in paper handling and naive stitching techniques, making the seams identifiable even with small deviations.
Innovation Solution
An image processing method that determines a path based on detected edges in the image to divide it into sub-images, using graph theory and alpha-blending to create complementary masks, which reduces the visibility of stitching lines by selecting elements that trick the human eye's pattern recognition, ensuring seamless integration of sub-images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If naive stitching techniques are used to combine sub-images, then the printing process is simple, but visible stitching lines appear between sub-images
Solution Approach 1:
The patent applies different processing treatments to different regions of the image. Specifically, it identifies stitching regions and applies blending operations (such as alpha blending or gradient blending) only in these local areas, while leaving other regions unchanged. This localised approach improves the quality at stitching lines without unnecessarily processing the entire image, thus resolving the contradiction between simple processing and high quality.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the image data before printing. It involves detecting potential stitching lines, calculating blending parameters, and preparing blended sub-images in advance. This preliminary processing ensures that when sub-images are combined, the stitching lines are already minimized or eliminated, achieving high quality without complex real-time stitching operations.
2Manufacturing precision
If overlapping regions between sub-images are increased to hide stitching lines, then stitching becomes less visible, but the stitching areas become more obvious and the overall image quality deteriorates
Solution Approach 1:
The patent dynamically adjusts the blending parameters (such as blend ratio, transition width, and blending function) based on the local image content and detected stitching line characteristics. Instead of using fixed overlapping regions, it optimizes the blending parameters to achieve the minimum necessary overlap for hiding stitching lines while preserving overall image quality. This parameter optimisation resolves the contradiction by finding the optimal balance point.
3Manufacturing precision
If precise paper handling is implemented to reduce stitching visibility, then stitching lines become less visible, but the device complexity and handling requirements increase
Solution Approach 1:
The patent replaces the need for precise mechanical paper handling with a software-based image processing solution. Instead of requiring complex mechanical systems to ensure perfect alignment, it uses computational methods (such as feature matching, perspective transformation, and adaptive blending) to correct misalignments and hide stitching lines in software. This substitution of mechanical precision with computational processing resolves the contradiction between stitching quality and device complexity.
Data Source
AI summary
In an example, an apparatus comprising an image processor is configured to perform edge detection on the image to detect a plurality of edges, automatically identify a path across the image based on the detected plurality of edges, divide the image into two adjacent sub-images based on the determined path, and generate first and second print data to cause the printing device to print the two adjacent sub-images.