Forward Interpolation Image Reconstruction Reducing Line Buffer Storage
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Solution Overview
Problem
The existing backward interpolation technique for reconstructing undistorted images from distorted images captured by a stationary lens system requires a large number of input line buffers, leading to increased storage and computational costs, and inefficient pixel estimation due to non-adjacent sampling in the input image.
Innovation Solution
The forward interpolation approach uses output line buffers to store pixel values for the reconstructed image, reducing the number of line buffers and storage requirements, and allows for re-use of sampled input values, thereby decreasing bandwidth and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If backward interpolation technique is used to reconstruct undistorted images, then image reconstruction is achieved, but the number of input line buffers increases leading to increased storage and computational costs
Solution Approach 1:
The patent inverts the traditional backward interpolation approach by implementing forward interpolation. Instead of mapping output pixels back to input pixels (backward mapping), the system maps input pixels forward to output pixels. This inversion fundamentally changes the buffering requirements, allowing the use of fewer line buffers while maintaining reconstruction quality. The forward mapping approach processes pixels in a sequential manner that naturally reduces memory requirements.
Solution Approach 2:
The patent changes the interpolation parameter approach by switching from backward mapping coordinates to forward mapping coordinates. This parameter change in the mapping direction enables a more efficient processing pipeline where input pixels are systematically transformed to output positions, reducing the need for extensive buffering of input data while preserving image reconstruction accuracy.
2Measurement precision
If backward interpolation technique is used, then image reconstruction is achieved, but computational costs and processing time increase
Solution Approach 1:
By inverting the interpolation direction from backward to forward mapping, the patent creates a more efficient processing flow. Forward mapping allows pixels to be processed in a natural sequential order from input to output, eliminating the need for complex coordinate transformations and multiple passes through the data, thereby reducing processing time while maintaining reconstruction quality.
Solution Approach 2:
The forward interpolation approach performs preliminary mapping of input pixel positions to output positions before actual interpolation occurs. This preliminary action establishes an efficient processing order and identifies which output pixels will be affected by each input pixel, allowing for optimized computation and reduced processing time compared to backward interpolation which requires on-the-fly coordinate calculations.
3Quantity of substance
If backward interpolation is used with non-adjacent sampling, then distorted image data is utilized, but pixel estimation precision decreases
Solution Approach 1:
The forward mapping approach inverts the sampling strategy by systematically determining which output pixels are influenced by each input pixel, rather than searching for input pixels for each output pixel. This inversion enables the method to handle non-adjacent sampling more effectively by tracking the actual spatial relationships between input and output pixels, thereby maintaining or improving pixel estimation precision despite the distorted image data source.
4Quantity of substance
If forward interpolation is used, then the number of line buffers is reduced, but new buffering architecture is required
Solution Approach 1:
The patent changes the buffering architecture parameters by transitioning from input-centric buffering to output-centric buffering. This parameter change in the buffering strategy aligns with the forward mapping approach, where output pixels are filled in a systematic order based on input pixel positions, naturally reducing the number of buffers needed while the new architecture simplifies the overall data flow and processing logic.
Data Source
AI summary
A forward interpolation approach is disclosed for enabling a second version of an image to be constructed from a first version of the image. According to one implementation, an input pixel from the first version of the image is forward mapped to the second version of the image to determine a set of candidate pixels that may be affected by the input pixel. Each candidate pixel is then backward mapped to the first version of the image to determine whether they are actually affected by the input pixel. For each candidate pixel that is actually affected by the input pixel, a pixel value is determined for that candidate pixel based at least in part upon the pixel value of the input pixel. By using this forward and backward mapping technique, forward interpolation can be implemented quickly and efficiently.