Multi-Camera Image Segmentation and Merging for Patch Reduction
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Solution Overview
Problem
Existing electronic devices with multiple cameras face challenges in generating high-quality image frames that effectively capture varying brightness and object regions, leading to suboptimal image quality and potential issues like patch phenomena and ghosting.
Innovation Solution
An electronic device with multiple cameras and neural network models that segment image frames into regions based on brightness and objects, apply camera parameter setting value sets to optimize image capture, and merge image frames to produce high-quality images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple cameras are used to capture images, then the quantity of captured image data increases, but the complexity of processing and merging these images increases
Solution Approach 1:
The patent divides the image frame into multiple regions based on brightness and object detection, then applies different camera parameter setting value sets to each region. This segmentation allows the system to manage multiple cameras and their respective parameter sets in an organized manner, reducing processing complexity while maintaining the quantity of captured image data.
Solution Approach 2:
The patent merges multiple image frames captured by different cameras into a single high-quality image frame. The merging process combines information from multiple sources while eliminating redundancy and artifacts, thus managing the quantity of image data without proportionally increasing processing complexity.
2Manufacturing precision
If camera parameter setting value sets are applied to optimize image quality, then image quality improves, but the processing time and computational resources increase
Solution Approach 1:
The patent pre-determines multiple camera parameter setting value sets based on region characteristics before actual image capture. By preparing these parameter sets in advance using neural network models that analyze brightness and object information, the system reduces real-time computational requirements while maintaining optimized image quality.
Solution Approach 2:
The patent dynamically adjusts camera parameters based on the detected brightness and object information in different regions. By changing parameters such as exposure, gain, and white balance according to regional characteristics, the system optimizes image quality without requiring excessive processing time, as the parameter adjustment is guided by pre-computed neural network predictions.
3Reliability
If image frames are merged to produce high-quality images, then patch and ghosting phenomena are reduced, but the complexity of image processing increases
Solution Approach 1:
The patent segments the image into regions with different brightness and object characteristics, then applies corresponding camera parameter sets to each region before merging. This segmentation approach ensures consistent image quality across different regions while simplifying the merging process, as each region has already been optimized with appropriate parameters.
Solution Approach 2:
The patent uses neural network models to predict optimal camera parameter setting value sets based on region characteristics. This feedback mechanism allows the system to automatically adjust parameters to minimize patch and ghosting phenomena during the merging process, reducing the complexity of manual processing while maintaining high image quality consistency.
Data Source
AI summary
An electronic device includes a plurality of cameras, and at least one processor connected to the plurality of cameras. The at least one processor is configured to, based on a first user command to obtain a live view image, segment an image frame obtained via a camera among the plurality of cameras into a plurality of regions based on a brightness of pixels and an object included in the image frame; obtain a plurality of camera parameter setting value sets, each including a plurality of parameter values with respect to the plurality of regions; based on a second user command to capture the live view image, obtain a plurality of image frames using the plurality of camera parameter setting value sets and at least one camera among the plurality of cameras; and obtain an image frame by merging the plurality of obtained image frames.