Light Field Image Motion Deblurring via Sub-Aperture PSF Modeling
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Solution Overview
Problem
Existing motion deblurring technologies, such as coded exposure photography (CEP), face challenges when applied to light field images, as they struggle to effectively predict and manage varying motions across multiple sub-aperture images in 3D data, which complicates the generation of a single optimal code for each motion.
Innovation Solution
A method is introduced that models the point spread function (PSF) for each sub-aperture image based on optical parameters and derives an optimization code using the argmax function to deblur motions in light field images, ensuring clear and precise data acquisition in 3D light field cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If coded exposure photography (CEP) is applied to light field images, then motion deblurring effectiveness is improved, but device complexity increases due to the need to model PSF for multiple sub-aperture images
Solution Approach 1:
The patent segments the light field image into multiple sub-aperture images and applies individual PSF modeling to each sub-aperture image. This segmentation approach allows the system to handle the complexity of 3D motion deblurring by breaking it down into manageable per-sub-aperture processing tasks, where each sub-aperture image can be deblurred independently based on its specific motion characteristics.
Solution Approach 2:
The patent applies local quality by deriving separate optimization codes for each sub-aperture image based on its specific PSF characteristics. Instead of using a single global code for all sub-aperture images, the system tailors the deblurring code to the local motion properties of each sub-aperture, thereby optimizing deblurring effectiveness for the specific 3D motion patterns present in different regions of the light field image.
2Device complexity
If a single optimization code is used for all sub-aperture images, then device complexity is reduced, but motion deblurring precision deteriorates due to varying motions across different sub-aperture images
Solution Approach 1:
The patent implements local quality by deriving distinct optimization codes for each sub-aperture image based on its specific PSF model. Since each sub-aperture image captures light from different angular perspectives and may exhibit different motion characteristics, the system customizes the deblurring code locally for each sub-aperture, thereby maintaining high deblurring precision across the entire light field image without requiring excessive system complexity.
Solution Approach 2:
The patent applies dynamics by making the optimization code adaptive to the specific motion characteristics of each sub-aperture image. Rather than using a static single code for all sub-apertures, the system dynamically generates appropriate codes based on the modeled PSF of each sub-aperture, allowing the deblurring process to adapt to varying 3D motion patterns across different spatial and angular dimensions.
Data Source
AI summary
A deblurring method of an embodiment is a method for deblurring a motion in a light field image in a processing device including at least one processor, and includes a first step of modeling PSF for a sub-aperture image of an acquired light field image on the basis of optical parameters, and a second step of deriving an optimization code on the basis of the obtained PSF of each sub-aperture image.