Dual Camera Image Alignment Vector Field Occlusion Correction
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Solution Overview
Problem
Dual camera imaging systems face challenges in aligning images due to occlusion areas, leading to image aliasing and blurring during High-Dynamic Range (HDR) and super resolution operations, as differences in camera angles result in missing content areas between images.
Innovation Solution
An image processing method that uses key point descriptors to form key point pairs, selects pairs based on depth information, generates an image alignment vector field, and corrects occlusion areas by eliminating alignment vectors in the vector field, ensuring accurate alignment and preventing image blurring or aliasing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If alignment operations are performed on occlusion areas in dual camera images, then image alignment is achieved, but image aliasing and blurring occur
Solution Approach 1:
The patent extracts and identifies occlusion areas in the image alignment vector field using depth information from dual cameras. By separating occlusion areas from non-occlusion areas, the method applies different processing strategies: alignment operations are performed on non-occlusion areas while occlusion areas are handled separately to avoid aliasing and blurring artifacts
Solution Approach 2:
The patent applies local quality by treating different regions of the image differently based on their occlusion status. Non-occlusion areas undergo full alignment operations for precise registration, while occlusion areas are identified and processed with special consideration to prevent degradation, ensuring each region receives appropriate processing quality
2Productivity
If HDR and super resolution operations are performed on occlusion areas, then image processing is completed, but image quality deteriorates due to aliasing and blurring
Solution Approach 1:
The patent performs preliminary action by identifying and marking occlusion areas in the image alignment vector field before executing HDR and super resolution operations. This preliminary identification allows subsequent processing steps to avoid applying harmful operations to occlusion areas, preventing aliasing and blurring while maintaining processing efficiency on valid regions
Solution Approach 2:
The patent converts the harmful effect of occlusion areas (which cause aliasing and blurring) into a benefit by using depth information to precisely identify these areas. This identification allows the system to selectively apply processing only where beneficial, turning the potential harm of occlusion into an opportunity for improved overall image quality through targeted processing
Data Source
AI summary
Obtaining a first set including key point descriptors in a first image that is of a to-be-shot scene and that is formed by using a first camera in a dual camera system, and a second set including key point descriptors in a second image that is of the to-be-shot scene and that is formed by using a second; pairing a key point in the first set and a key point in the second set that match each other to form a key point pair; selecting the key point pair that meets a predetermined condition; generating an image alignment vector field according to the selected key point pair; and estimating an occlusion area according to the depth information of the first image and the second image, and eliminating an alignment vector of the occlusion area in the image alignment vector field, to form a corrected image alignment vector field.