Multi-Focal Image Reconstruction for Parallel Depth Capture
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Solution Overview
Problem
Conventional image recognition methods require extensive time and resources to capture clear images of multiple target objects, as each object needs to be focused and shot individually, leading to low efficiency in image capturing and subsequent recognition operations.
Innovation Solution
The method involves capturing multiple images using different focal lengths simultaneously or sequentially, recognizing target areas corresponding to these focal lengths, and generating a single reconstructed image with all object images in focus, without the need for image analysis or stitching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple target objects are captured using conventional focusing methods, then each target object can be clearly focused, but the time and resources required increase significantly
Solution Approach 1:
The image capturing process is segmented by dividing the scene into multiple depth ranges, with each image capturing device assigned to capture a specific depth range. This segmentation allows parallel processing of multiple targets at different depths simultaneously, eliminating the sequential focusing time required by conventional methods.
Solution Approach 2:
The patent introduces a new dimension of depth range allocation by assigning different focal lengths to different devices, transforming the single-dimension sequential focusing approach into a multi-dimensional parallel capturing system. This dimensional expansion enables simultaneous capture of multiple depth planes.
2Measurement precision
If each target object is focused and shot individually, then clear images of each object are obtained, but the overall efficiency of image capturing operation decreases
Solution Approach 1:
Multiple image capturing devices are merged into a coordinated system where each device captures a specific depth range. The captured images are then combined through synthesis to produce a final image with all targets in focus, achieving both image clarity and operational efficiency simultaneously.
Solution Approach 2:
The system achieves multi-functionality by enabling a single capturing operation to simultaneously capture multiple depth ranges that would traditionally require separate focusing operations. This universal approach maintains image clarity while dramatically improving capturing efficiency.
3Loss of information
If multiple images of target objects are captured and synthesized, then complete recognition data is obtained, but the consumption of time and computing resources increases
Solution Approach 1:
The system performs preliminary action by pre-allocating depth ranges to specific image capturing devices before capture. This preliminary organization ensures that each device captures only the depth range it is optimized for, reducing redundant data and simplifying the subsequent synthesis process, thereby maintaining information completeness while reducing synthesis time.
Data Source
AI summary
An image capturing method is provided. The method includes: shooting a monitoring scene at the same time through multiple image capturing devices to capture multiple images corresponding to multiple focal lengths at a same time point; recognizing a target area of each of the captured images according to multiple focal sections; keeping multiple target sub-images in the target areas of the images, in which multiple object images in the target sub-images are all focused; directly generated a single reconstructed image corresponding to the time point according to the target sub-images; and outputting the reconstructed image.