Camera Region-Specific Exposure Optimization for Motion Blur
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Solution Overview
Problem
Existing camera systems face challenges in optimizing exposure times for image frames, leading to blurry high-motion events and poor detail capture in low-motion events, especially in dim light conditions, and require costly multi-camera setups to address these issues.
Innovation Solution
A method and camera system that determine regions of varying motion levels within a scene, allowing for region-specific exposure times by combining image data from multiple frames, enhancing signal-to-noise ratio for low-motion regions and preventing blurriness in high-motion areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a long exposure time is used to capture low-motion events with high detail, then the signal-to-noise ratio is improved, but high-motion events become blurry
Solution Approach 1:
The image frame is divided into multiple regions, each corresponding to different scene regions with different motion levels. Each region can be processed with different exposure times independently, allowing low-motion regions to use long exposure for detail while high-motion regions use short exposure to avoid blur
Solution Approach 2:
Different exposure times are applied to different regions of the image frame based on the motion characteristics of each region. This allows each region to have optimal exposure settings tailored to its specific motion level, rather than using a single uniform exposure time for the entire frame
2Reliability
If a short exposure time is used to capture high-motion events without blur, then motion events are captured clearly, but low-motion events lose detail and have poor signal-to-noise ratio
Solution Approach 1:
The image frame is segmented into regions with different motion characteristics. High-motion regions use short exposure times to capture motion events clearly without blur, while low-motion regions use long exposure times to accumulate sufficient light for detailed capture
Solution Approach 2:
The exposure time becomes a dynamic parameter that varies across different regions of the image frame based on motion detection. The system adaptively adjusts exposure settings for each region rather than using a fixed exposure time, allowing optimal capture of both high-motion and low-motion events
3Reliability
If two or more cameras with different exposure times are used to capture both high-motion and low-motion events, then both types of events can be captured with appropriate exposure, but the system becomes costly and complex
Solution Approach 1:
Instead of using multiple cameras, the patent segments a single camera's image frame into multiple regions and applies different exposure processing to each region through computational methods, achieving the same effect as multiple cameras with different exposure times but with a single camera system
Solution Approach 2:
The patent uses computational imaging techniques to create multiple exposure versions from a single camera by processing different regions with different exposure parameters, effectively copying the functionality of multiple cameras without the hardware overhead
Data Source
AI summary
A method and camera are used for optimizing the exposure of an image frame in a sequence of image frames capturing a scene based on level of motion in the scene. Based on image data from a plurality of image sensor frames, regions of the scene are determined including different level of motion. Image frame regions for the image frame are determined, wherein an image frame region corresponds to at least one region of the scene. The exposure of the image frame is optimized by emulating a region specific exposure time for each image frame region by producing each image frame region using image data from a number of image sensor frames. The number of image sensor frames used to produce a specific image frame region is based on the level of motion in the at least one corresponding region of the scene.