Multi-Camera Image Fusion With Diffusion Kernels for Seamless Zoom Transitions
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Solution Overview
Problem
Existing image processing systems in electronic devices face challenges with high resource demands, limited detail in some images, and inadequate fusion of images from multiple cameras, leading to noisy and disjointed transitions between different focal lengths.
Innovation Solution
A method and system for aligning and fusing images from multiple cameras using a diffusion kernel and adaptive bandwidth averaging, guided by reference image structure, to enhance image quality and maintain seamless transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If images from multiple cameras with different focal lengths are captured and processed, then image detail and field of view are improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the image processing task by dividing the field of view into multiple regions, each captured by cameras with different focal lengths. The processor selectively processes only the regions that require enhancement rather than processing entire images, reducing computational load while maintaining image detail in critical areas.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the image based on their importance. High-detail regions are processed with greater computational effort while less critical regions receive minimal processing, optimizing the balance between image quality and processing time.
2Reliability
If images from multiple cameras are fused, then image quality and noise reduction are improved, but processing complexity increases
Solution Approach 1:
The patent applies partial fusion by selectively combining images from multiple cameras only in regions where it provides benefit. Rather than fusing all images completely, the system performs fusion only where needed to reduce noise and improve quality, reducing processing complexity while maintaining reliability in critical areas.
3Reliability
If adaptive bandwidth averaging is applied, then noise reduction is improved, but computational resources and processing time increase
Solution Approach 1:
The adaptive bandwidth averaging is applied selectively to specific regions and frames rather than uniformly across all image data. The system adjusts the bandwidth parameter dynamically based on local image characteristics, applying stronger noise reduction only where necessary, thus improving noise reduction effectiveness while reducing overall computational resource consumption.
Data Source
AI summary
A method performed by an electronic device is described. The method includes obtaining a first image from a first camera, the first camera having a first focal length and a first field of view. The method also includes obtaining a second image from a second camera, the second camera having a second focal length and a second field of view disposed within the first field of view. The method further includes aligning at least a portion of the first image and at least a portion of the second image to produce aligned images. The method additionally includes fusing the aligned images based on a diffusion kernel to produce a fused image. The diffusion kernel indicates a threshold level over a gray level range. The method also includes outputting the fused image. The method may be performed for each of a plurality of frames of a video feed.