Block-Based Digital Image De-Warping for Embedded Systems
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Solution Overview
Problem
Conventional de-warping methods for panoramic camera images require significant computational resources, making them unsuitable for real-time processing on embedded systems and increasing the cost and scalability issues of monitoring systems.
Innovation Solution
A de-warping processing method that segments images into blocks and sub-blocks, calculates coordinate transformation parameters based on vertex coordinates, and applies affine or perspective transformations to restore deformed images efficiently, reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional de-warping processing method is used, then de-warping accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the image into multiple blocks and processes each block independently using simplified coordinate transformation. This segmentation approach reduces the overall computational complexity while maintaining acceptable de-warping accuracy for each local region, resolving the contradiction between accuracy and computational burden.
Solution Approach 2:
The patent uses pre-defined transformation models and lookup tables that can be quickly applied without complex real-time calculations. These simplified transformation parameters act as 'cheap' computational solutions that provide sufficient accuracy for embedded systems with limited processing power.
2Measurement precision
If conventional de-warping processing method is used, then de-warping quality is improved, but processing time increases
Solution Approach 1:
By dividing the image into blocks and processing them in parallel or sequential order with simplified algorithms, the total processing time is reduced while maintaining acceptable quality standards for each block, enabling real-time or near-real-time de-warping performance.
Solution Approach 2:
The patent applies de-warping processing selectively to specific regions or blocks rather than attempting perfect global optimization. This partial action approach achieves sufficient quality for monitoring purposes while significantly reducing the time required compared to comprehensive de-warping methods.
3Productivity
If high-performance computer is used for de-warping, then processing capability is improved, but system cost increases
Solution Approach 1:
The patent enables embedded systems to perform de-warping processing independently using optimized algorithms and pre-computed transformation parameters. This self-service capability eliminates the need for external high-performance computers, reducing system cost while maintaining adequate processing capability for monitoring applications.
Solution Approach 2:
The patent transforms the de-warping problem into a set of parameter-based transformations that can be efficiently computed with simple arithmetic operations. By changing the approach from complex iterative optimization to parameter-based direct calculation, the processing capability is maintained at the embedded system level without requiring expensive hardware.
4Measurement precision
If comprehensive de-warping processing is applied, then image restoration quality is improved, but system scalability is reduced
Solution Approach 1:
The block-based processing approach allows the system to scale by adjusting the number and size of blocks according to computational resources available. Different configurations can be deployed across various embedded platforms, maintaining image restoration quality while adapting to different system capabilities, thus improving scalability.
Data Source
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Figure 3A~3B
AI summary
A de-warping processing method for digital images includes the following steps. A first image window is segmented into a plurality of first blocks. A second image window is segmented into a plurality of second blocks according to locations and an amount of the first blocks. A coordinate transformation parameter is calculated by a group of first vertex coordinate values of the first block and a group of second vertex coordinate values of the corresponding second block. Each of coordinate values of the first block is transformed into an estimated coordinate value of the corresponding second block according to the coordinate transformation parameter. A pixel content value is obtained from the second image window according to each of the estimated coordinate values, and is written into a pixel with the corresponding coordinate value in the first block. All the first blocks are processed for outputting a first image.