Multi-Sensor Image Disparity Model for Seamless Sensor Switching
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Solution Overview
Problem
Multi-sensor image capture devices experience artifacts due to switching between image sensors with different fields of view, leading to disruptions in image frame continuity, such as scene shifts in videos.
Innovation Solution
The use of a disparity model to predict and adjust image frames, allowing for geometric warping to match the field of view of one image sensor to another, thereby reducing artifacts by using predicted disparity values when actual values are unavailable or erroneous.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple image sensors with different fields of view are used to capture images, then the device can provide multiple fields of view and zoom levels, but artifacts such as scene shifts occur during sensor switching
Solution Approach 1:
The system performs preliminary calibration to determine transformation matrices between different image sensors before actual image capture. These pre-computed transformation matrices are stored and applied during sensor switching to predict and correct the field of view changes, preventing scene shift artifacts from occurring in the first place
Solution Approach 2:
The patent introduces transformation matrices as an intermediary computational element that mediates between different image sensors with different fields of view. These matrices act as a bridge to translate and align images from one sensor to another, ensuring seamless transitions without visible artifacts
2Adaptability or versatility
If multiple image sensors are used to capture image data, then different zoom levels can be provided, but the complexity of image processing increases
Solution Approach 1:
The system performs preliminary calibration to determine and store transformation matrices between different image sensors before actual image capture. These pre-computed matrices eliminate the need for complex real-time calculations during sensor switching, reducing processing complexity while maintaining versatility
Solution Approach 2:
The patent transforms the complex problem of multi-sensor image processing into a parameter-based solution by representing field of view relationships through transformation matrices. This parameterization simplifies the processing by reducing it to matrix multiplication and coordinate transformation operations
3Ease of operation
If images from multiple sensors are processed to generate a single image, then a unified output can be provided, but transitions between sensors become perceptible to the human eye
Solution Approach 1:
The system performs preliminary calibration to determine transformation matrices that map between different sensor fields of view. These pre-computed matrices enable seamless blending during sensor transitions, making the transitions imperceptible to the human eye while maintaining a unified single image output
Solution Approach 2:
The patent creates a transformed copy of the image from one sensor that matches the field of view of another sensor. By generating this transformed copy using pre-computed transformation matrices, the system enables smooth transitions that are imperceptible to human observers
Data Source
AI summary
Artefacts in a sequence of image frames may be reduced or eliminated through modification of an input image frame to match another image frame in the sequence, such as by geometrically warping to generate a corrected image frame with a field of view matched to another frame in sequence of frames with the image frame. The warping may be performed based on a model generated from data regarding the multi-sensor device. The disparity between image frames may be modeled based on image captures from the first and second image sensor for scenes at varying depths. The model may be used to predict disparity values for captured images, and those predicted disparity values used to reduce artefacts resulting from image sensor switching. The predicted disparity values may be used in image conditions resulting in erroneous actual disparity values.