Realtime Image Analysis and Feedback for Mobile Long Exposure
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Solution Overview
Problem
Mobile devices face challenges in capturing high-quality long exposure images without the need for tripods or additional equipment, due to their limited processing capabilities and the difficulty of maintaining camera stability during extended exposures.
Innovation Solution
The techniques involve a multi-phase processing approach on mobile devices, including image acquisition, alignment, and merging of multiple short exposures to create a single long exposure image, utilizing hardware components like HDR and optical image stabilization, and leveraging machine learning for real-time feedback and image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple short exposures are captured and processed to create long exposure images, then image quality and noise reduction are improved, but processing time and computational load increase
Solution Approach 1:
The patent divides the long exposure capture process into multiple discrete short exposure frames that are captured sequentially. Each frame is processed independently through alignment and merging operations, allowing the system to manage computational load in manageable segments rather than attempting to process a single continuous long exposure.
Solution Approach 2:
The system performs preliminary actions by capturing multiple short exposure frames in advance before final merging. During capture, the device pre-aligns frames using motion sensors and performs preliminary processing to reduce noise and stabilize images, so that the final merging operation requires less intensive computation.
2Reliability
If multiple frames are captured and merged to create long exposure images, then unwanted elements are reduced, but device complexity and processing requirements increase
Solution Approach 1:
The patent introduces intermediary components including motion sensors (accelerometers, gyroscopes) that act as mediators between the camera and the image processing pipeline. These sensors provide motion data that intermediates the alignment process, allowing frames to be stabilized and merged more efficiently without requiring complex computational algorithms alone.
Solution Approach 2:
The system replaces purely mechanical/image-based stabilization methods with sensor-based motion detection. Instead of relying solely on image analysis to detect and correct motion, the patent uses motion sensors to directly measure device movement and apply corresponding corrections during frame alignment, reducing the computational complexity of image-based stabilization.
3Measurement precision
If real-time feedback is provided during image capture, then user control and image quality are improved, but processing power consumption increases
Solution Approach 1:
The patent applies partial processing to provide real-time feedback during capture. Instead of performing complete image processing on all captured frames, the system processes only selected frames or performs simplified analysis to generate feedback indicators (such as stability metrics or exposure quality measures), consuming less power while still providing useful real-time information to the user.
Data Source
AI summary
Performing realtime image analysis and providing realtime feedback are disclosed. A stream comprising a plurality of arriving RAW images is received. A RAW image included in the stream is sent to a graphics processing unit (GPU). A result of the GPU is used to generate a visualization corresponding to the RAW image. The visualization is co-presented with a realtime view of a scene in a display.