Multi-Camera Framing Recommendation for Non-Standard Scenes
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Solution Overview
Problem
Current mobile electronic devices struggle with poor image composition and quality due to limitations in scene type and composition templates, especially when there is no foreground, and existing methods fail to provide effective framing recommendations for non-standard scenes.
Innovation Solution
A photographing method and apparatus that utilize deep learning to analyze images from multiple cameras with different focal lengths, providing framing recommendations through cropping and rotation to optimize composition and field of view, enhancing image quality and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If switching between wide-angle lens and long-focus lens is performed based on composition template, then composition recommendation is provided, but image quality deteriorates due to cropping or zooming of single lens image
Solution Approach 1:
The patent merges images captured by multiple cameras with different focal lengths through image fusion technology to generate a composite image that maintains high quality while providing composition recommendations, avoiding the quality degradation associated with cropping or zooming single lens images
2Ease of operation
If composition template matching is used for intelligent framing, then framing recommendation is provided, but recommendation capability deteriorates for scenes without foreground
Solution Approach 1:
The patent employs deep learning algorithms that can universally handle various scene types including scenes without foreground by learning from diverse training data, enabling the system to provide accurate framing recommendations across all scene types rather than relying on limited composition templates
3Manufacturing precision
If multiple cameras with different focal lengths are used for joint imaging, then photographing quality is improved, but device complexity increases
Solution Approach 1:
The system automatically performs image fusion and framing optimization through deep learning algorithms without requiring manual intervention, enabling the complex multi-camera system to operate autonomously and simplify the user experience despite the underlying complexity
Data Source
AI summary
The present disclosure relates to the field of image processing technologies, and discloses a photographing method and apparatus for intelligent framing recommendation, to automatically match an appropriate framing recommendation solution for a user according to an algorithm, so that imaging quality is improved. The method includes: An electronic device obtains at least one framing recommendation result based on images captured by at least two cameras with different focal lengths, where each of the at least one framing recommendation result includes a framing recommendation frame, and the framing recommendation frame indicates a framing recommendation effect of image photographing; and displays a target image based on a framing recommendation result selected by a user among the at least one framing recommendation result, where the target image is obtained through cropping, based on the selected framing recommendation result, the images captured by the at least two cameras.