Multi-Exposure Image Fusion for Motion Blur and Noise Reduction
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Solution Overview
Problem
Handheld digital cameras often produce images with motion blur due to camera shake and increased noise from high ISO settings, especially when capturing scenes with multiple sources of motion, as existing exposure parameter settings typically account for only one source of motion.
Innovation Solution
The method involves capturing images using multiple exposure parameter sets, one for global motion and one or more for local motion, and then fusing these images to create a composite image that reduces blurring of moving objects and noise, by combining unblurred portions from the first image with reduced noise portions from the second image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If exposure time is increased to reduce noise, then image brightness is improved, but motion blur increases due to camera shake and moving objects
Solution Approach 1:
The image is divided into multiple regions based on motion characteristics. Global motion regions (camera shake) are separated from local motion regions (moving objects). Different exposure parameters are applied to different regions, allowing longer exposure for global motion to reduce noise while using shorter exposure for local motion to prevent blur.
Solution Approach 2:
The system dynamically adjusts exposure parameters based on detected motion characteristics. Motion estimation algorithms analyze the scene to determine regions with global motion versus local motion, and exposure time is adaptively adjusted for each region to optimize the trade-off between noise reduction and motion blur prevention.
2Manufacturing precision
If exposure parameters are adjusted based on overall motion to prevent blur, then image sharpness is improved, but noise increases due to shorter exposure time
Solution Approach 1:
Different quality characteristics are applied to different parts of the image. Regions identified as having only global motion (camera shake) are processed with longer exposure parameters to maximize brightness, while regions with local motion (moving objects) use shorter exposure parameters to maintain sharpness. This local differentiation resolves the contradiction by allowing both high brightness and high sharpness in different regions.
3Device complexity
If a single exposure parameter set is used for the entire image, then device complexity is reduced, but image quality deteriorates when multiple sources of motion are present
Solution Approach 1:
The image processing system segments the scene into different motion regions using motion estimation techniques. By identifying regions with global motion versus local motion, the system can apply different exposure parameters to each region, thereby maintaining high image quality in the presence of multiple motion sources without requiring completely separate exposure control systems.
Solution Approach 2:
The system changes exposure parameters (particularly exposure time) based on the detected motion characteristics of different image regions. By dynamically adjusting parameters according to motion analysis, the system achieves high image quality for multiple motion sources while keeping the overall device complexity manageable through a unified processing framework.
Data Source
AI summary
A system and method for capturing images is provided. In the system and method, preview images are acquired and global local and local motion are estimated based on at least a portion of the preview images. If the local motion is less than or equal to the global motion, a final image is captured based at least on an exposure time based on the global motion. If the local motion is greater than the global motion, a first image is captured based on at least a first exposure time and at least a second image is captured based on at least one second exposure time less than the first exposure time. After capturing the first and second images, global motion regions are separated from local motion regions in the first and second images, and the final image is reconstructed at least based on the local motion regions.