Image Alignment via Binary Thresholding and Characteristic Pixel Extraction
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Solution Overview
Problem
Existing image alignment methods, such as SIFT, are time-consuming due to the need for building multiple Gaussian blur layers to find characteristic pixels, which decreases the efficiency of aligning images captured with different intensity ranges without a tripod, leading to fuzzy combined images.
Innovation Solution
An image processing method and device that maps images to binary space to efficiently acquire representative characteristic pixels by generating thresholds based on pixel intensities, converting images into binary form, and aligning them using these pixels, thereby simplifying the process of image alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scale-invariant feature transform (SIFT) is used to align images, then alignment accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent extracts only the essential information needed for alignment by converting images to binary form and selecting only the most distinctive characteristic pixels, rather than processing all pixel data through complex Gaussian blur layers. This extraction approach maintains alignment accuracy while significantly reducing processing time.
Solution Approach 2:
The patent changes the parameter space from continuous intensity values to binary (0/1) representations. By transforming images into binary form and working with simplified pixel intensity parameters, the system achieves fast processing without sacrificing the ability to identify distinctive features for accurate alignment.
2Measurement precision
If multiple Gaussian blur layers are built to find characteristic pixels, then characteristic pixel identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the necessary computational steps by using binary image conversion and simple pixel intensity comparison, eliminating the need for complex multi-layer Gaussian blur computations. This extraction maintains the ability to identify characteristic pixels while dramatically reducing computational complexity.
Solution Approach 2:
The patent creates a simplified copy of the image in binary form that preserves the essential structural information needed for feature identification. This binary copy serves as an efficient surrogate that requires much less computational processing than the original multi-layer Gaussian blur approach.
Data Source
AI summary
An image processing method for image alignment, includes sequentially receiving a plurality of images; generating at least one threshold corresponding to each image according to a plurality of intensities of each image of the plurality of images; converting each image according to the thresholds of each image, for generating a plurality of binary images; acquiring a plurality of characteristic pixels of each binary image according to the plurality of binary images; and aligning the plurality of images according to the plurality of characteristic pixels of each binary image.