Multimodal Image Fusion Using Sub-Image Weighting for Clearer Outlines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image fusion methods suffer from unclear target object outlines, fuzzy objects, and loss of details, leading to poor image fusion quality.
Innovation Solution
A method involving multiple imaging techniques (PET/SPECT and MRI) to construct sub-images based on pixel value distributions and thresholds, followed by weighted summation using specific weight matrices to enhance and fuse images, improving image clarity and detail retention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image fusion methods are used, then the fusion process is simple, but the target object outline is unclear and details are lost
Solution Approach 1:
The patent divides each input image into multiple sub-images based on pixel value distribution thresholds. For example, the first image is segmented into first sub-image and second sub-image, and the second image is segmented into third sub-image and fourth sub-image. This segmentation allows different regions of the image to be processed and fused separately, improving the clarity of target object outlines and retaining detailed information while managing complexity through structured processing.
Solution Approach 2:
The patent applies different weight matrices to different sub-images during the fusion process. Specifically, first and second weight matrices are applied to the first and second sub-images, while third and fourth weight matrices are applied to the third and fourth sub-images. This local quality approach allows optimization of fusion parameters for different regions, enhancing overall image fusion quality by addressing the specific characteristics of each sub-region.
2Manufacturing precision
If conventional image fusion methods are used, then the processing time is short, but the target object appears fuzzy and details are lost
Solution Approach 1:
By segmenting images into sub-images based on pixel value distributions, the patent can process and fuse different regions with appropriate detail preservation techniques. This segmentation enables selective attention to regions requiring detail retention, improving overall detail preservation without requiring excessive processing time across the entire image.
Solution Approach 2:
The patent changes fusion parameters dynamically by applying different weight matrices to different sub-images. This parameter adaptation allows optimization of detail retention in specific regions while maintaining efficient processing in other regions, balancing detail preservation with processing time requirements.
Data Source
AI summary
A method of processing an image includes: acquiring a first image for a target object by using a first imaging method (S110); acquiring a second image for the target object by using a second imaging method (S120); constructing a first sub-image and a second sub-image based on the first image (S130); constructing a third sub-image and a fourth sub-image based on the second image (S140); and determining a pixel value distribution information of a fused image based on a pixel value distribution information of each of the first sub-image, the second sub-image, the third sub-image, and the fourth sub-image, so as to obtain the fused image (S150). An apparatus of processing an image, a computing device, and a computer-readable storage medium are further provided.