Imaging Apparatus Mask Selection for Depth Estimation Accuracy
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Solution Overview
Problem
The existing depth from defocus (DFD) technique struggles with accurate depth estimation for edge images with edge directions close to the positional difference direction of the masks used, leading to low depth estimation accuracy.
Innovation Solution
An imaging apparatus and method that involves obtaining a non-masked captured image, determining a representative edge direction, selecting a combination of masks with high depth estimation accuracy for that edge direction, capturing images using those masks, and performing decoding to estimate the subject's depth at multiple positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two masks with apertures in different positions are used for coded imaging, then depth estimation can be performed, but depth estimation accuracy is low for edge images with edge directions close to the positional difference direction of the masks
Solution Approach 1:
The invention divides the mask set into multiple groups, where each group contains masks suitable for specific edge directions. By segmenting the mask functionality, the system can select the appropriate group based on the detected edge direction, thereby maintaining high depth estimation accuracy across various edge orientations without requiring a single universal mask configuration
Solution Approach 2:
The invention introduces dynamic mask selection based on the detected edge direction. Instead of using fixed masks, the system dynamically chooses masks from different groups according to the representative edge direction determined from the captured image, allowing the depth estimation process to adapt to different edge orientations and maintain high accuracy
2Measurement precision
If multiple masks are prepared for different edge directions, then depth estimation accuracy improves, but device complexity increases
Solution Approach 1:
The invention performs preliminary classification of masks into groups based on their suitable edge directions before the actual depth estimation process. This preliminary organization allows for efficient mask selection during imaging, reducing the complexity of managing multiple masks while maintaining high depth estimation accuracy for different edge directions
Solution Approach 2:
The invention implements a feedback mechanism where the detected representative edge direction from the captured image feeds back into the mask selection process. This feedback loop enables the system to automatically select the most appropriate mask group based on the actual image content, simplifying the overall device operation while achieving high accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more stable and accurate depth estimation for edge images in the representative edge direction, enhancing the practicality of the DFD technique.
Implementation Method 1
an optical system on which light from a subject is incident
Implementation Method 2
an optical system on which light from a subject is incident; an imaging element which receives the light passing through the optical system
Implementation Method 3
a mask installation unit which creates a state in which any of a plurality of masks prepared in advance is installed and a state in which none of the masks is installed in an incident region of the light incident on the optical system from the subject
Data Source
AI summary
Provided is a subject depth estimation method including the steps of obtaining a non-masked captured image by imaging a subject in a state where no mask is installed, determining a representative edge direction based on an edge image included in the non-masked captured image, selecting, from among a plurality of masks prepared in advance, a combination of the masks with a relatively highest depth estimation accuracy of an object corresponding to an image representing an edge in an equal direction to the representative edge direction, obtaining a plurality of masked captured images by imaging the subject using each of the masks included in the selected combination of the masks, and calculating a depth estimation value of the subject at each of a plurality of positions by performing decoding processing based on a point spread function unique to each of the masks selected on the plurality of masked captured images.