Multi-Camera Image Alignment for Lens Reflection Artifact Removal
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Solution Overview
Problem
Existing methods for removing camera lens reflection artifacts, such as bubble artifacts, are inefficient and costly, often requiring post-production editing or AI/ML techniques that are time-consuming and prone to inaccuracies, and do not effectively predict artifact locations.
Innovation Solution
A multi-camera device system that detects bright light sources, captures images with different field of views, aligns and subtracts them to identify artifact locations, and generates a masked binary image to remove artifacts, using dynamic thresholding and restoration techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI/ML methods are used to remove bubble artifacts, then artifact removal capability is improved, but computational complexity and training requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by capturing multiple images with different fields of view before the actual image capture, specifically capturing a first image with a first field of view and a second image with a second field of view. This allows the bubble artifact location to be determined in advance through image subtraction, avoiding the need for complex AI/ML processing during or after the main capture operation.
Solution Approach 2:
The system uses a copy of the captured scene from a different field of view (the second image) to identify and remove artifacts from the original image (the first image). By capturing an alternative view of the same scene, the system can subtract the two images to reveal bubble artifact locations without requiring complex computational models.
2Reliability
If post-production editing is used to remove bubble artifacts, then artifact removal is achieved, but time consumption and effort increase
Solution Approach 1:
The system performs artifact location determination before the main image capture by capturing preliminary images with different fields of view. The bubble artifact location is identified through image subtraction of these preliminary images, allowing the actual image capture to proceed without subsequent post-production editing time.
Solution Approach 2:
The system uses the captured images themselves as the tool for artifact removal. By subtracting the first image from the second image (which has a different field of view), the system automatically generates a masked binary image that identifies bubble artifact locations, eliminating the need for external post-production editing tools or manual intervention.
3Object-affected harmful factors
If anti-reflective coating is applied to the lens, then glare is reduced, but bubble artifacts still appear randomly and additional costs are incurred
Solution Approach 1:
The system replaces the physical approach (anti-reflective coating) with an image processing approach. Instead of relying on optical coatings to prevent bubble artifacts, the system captures images with different fields of view and uses computational subtraction to identify and remove bubble artifacts, providing more reliable and complete artifact removal.
Solution Approach 2:
The system uses a copy of the scene from a different field of view to identify and remove artifacts. By capturing a second image with a different field of view and subtracting it from the first image, the system can accurately locate bubble artifacts without relying on optical coatings, achieving more reliable artifact elimination.
4Measurement precision
If multiple cameras with different fields of view are used, then artifact location prediction is improved, but device complexity increases
Solution Approach 1:
The system segments the imaging function into multiple cameras with different fields of view, where each camera captures a specific portion of the scene. The first camera captures the main scene while the second camera captures an alternative view, allowing the bubble artifact location to be determined through image subtraction without requiring complex processing of a single camera's data.
Solution Approach 2:
The multi-camera system serves multiple functions: the first camera captures the primary image for output, while the second camera captures an alternative view used for artifact identification. This universal approach allows the same hardware (multiple cameras) to serve both imaging and artifact detection purposes, reducing the need for additional specialized equipment.
Data Source
AI summary
A method for removing one or more artifacts from an image of a scene includes detecting a presence of a light beyond a predefined brightness in the scene; capturing a first image of the scene using a first Field of View (FOV) of a first camera of a multi-camera device, and a second image of the scene using a second FOV of a second camera of the multi-camera device; modifying the second image to generate an aligned second image by matching the first FOV with the second FOV; generating a masked binary image by subtracting the first image from the aligned second image, wherein the masked binary image indicates one or more locations of the one or more artifacts in the first image; and outputting a third image generated by removing the one or more artifacts from the first image using the aligned second image and the masked binary image.