Multi-Stage Image Mapping Mechanism for Homography Rectification
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Solution Overview
Problem
Conventional image mapping techniques for rectifying homographic distortions are computationally expensive and inefficient in terms of memory access, as they require independent identification of corresponding pixels in distorted and rectified images, leading to increased computational complexity and time consumption.
Innovation Solution
A multi-stage image mapping mechanism that decomposes the homography transformation into two separate transformations, allowing for efficient pixel correspondence identification and memory access by mapping pixels in a distorted image to a rectified image through a two-stage process, where the first stage removes partial distortion to generate an intermediate image and the second stage removes residual distortion to produce a rectified image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image mapping techniques are used to rectify homographic distortions, then accurate pixel correspondence can be achieved, but computational complexity and computing time increase significantly
Solution Approach 1:
The patent segments the image mapping process into distinct stages: identifying anchor pixels, computing transformation parameters, and applying the transform to remaining pixels. This segmentation allows the complex homographic rectification to be broken down into manageable steps, reducing overall computational complexity while maintaining accuracy through systematic processing
2Measurement precision
If conventional image mapping techniques are used to rectify homographic distortions, then accurate pixel correspondence can be achieved, but computing time increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-identifying anchor pixels and pre-computing transformation parameters before applying the full transform. This preliminary computation of key reference points enables faster processing of the remaining pixels, reducing overall computing time while maintaining accuracy
Solution Approach 2:
The patent uses anchor pixels as reference copies that define the transformation. Once the transform parameters are computed from anchor pixels, these parameters are copied and applied to transform all other pixels, avoiding redundant computation for each individual pixel while maintaining consistent accuracy
3Measurement precision
If independent pixel identification is performed for each pixel, then accurate mapping can be achieved, but memory access efficiency deteriorates
Solution Approach 1:
The patent merges the transformation operation into a unified process where a single homographic transform parameter set is applied to all pixels simultaneously. This combining of the transform operation allows for optimized memory access patterns, as pixels can be processed in batches or sweeps rather than independent random accesses, improving memory access efficiency while maintaining mapping accuracy
Data Source
AI summary
The present disclosure provides a multi-stage image mapping mechanism for mapping a distorted image to a rectified image. For example, the multi-stage image mapping mechanism can remove homography from a distorted image to reconstruct a rectified image in two-stages: (1) a first stage in which distortion is partially removed from a distorted image to generate an intermediate image, and (2) a second stage in which residual distortion is removed from the intermediate image to recover the rectified image.