Image Interpolation Using Weighted Edge Direction Sub-arrays
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Solution Overview
Problem
Existing image interpolation algorithms, such as nearest neighbor and bilinear interpolation, result in noticeable jaggies and artificial traces when enlarging images, and edge-detection based methods are computationally complex and inaccurate in complicated regions.
Innovation Solution
A method that determines the proximity of edge directions in multiple preset directions at the interpolation point, using weighted interpolation pixel values calculated from multiple pixel sub-arrays to reduce distortion and jaggies, rather than relying on accurate edge detection, and combines interpolation results from different sub-arrays to avoid sharp edge artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional interpolation algorithms (nearest neighbor, bilinear, cubic) are used to enlarge images, then the interpolation process is simple and fast, but the output image has obvious jaggies and artificial traces
Solution Approach 1:
The patent segments the interpolation process by dividing the image into different regions (edge regions and non-edge regions) and applying different interpolation strategies to each region. Edge detection is performed to identify regions requiring special handling, and separate interpolation calculations are conducted for edge and non-edge areas, thereby improving overall image quality while managing computational complexity through region-based processing
Solution Approach 2:
The patent applies local quality by using different interpolation methods for different regions of the image. Specifically, edge regions use one interpolation approach while non-edge regions use another, allowing each local area to be processed with the most appropriate method for its characteristics, thus improving manufacturing precision (image quality) without uniformly increasing complexity across the entire image
2Manufacturing precision
If edge-detection based interpolation methods are used to reduce jaggies, then image quality improves, but computational complexity increases and accuracy decreases in complicated regions
Solution Approach 1:
The patent applies partial action by performing edge detection only in regions where it is most beneficial, rather than uniformly across the entire image. The method selectively applies complex edge-detection-based interpolation only where needed, while using simpler methods elsewhere, thus improving image quality while controlling computational complexity by avoiding excessive processing in all regions
3Manufacturing precision
If weighted interpolation using multiple pixel sub-arrays is applied, then jaggies and artificial traces are reduced, but calculation complexity increases
Solution Approach 1:
The patent segments the pixel array into multiple sub-arrays and performs weighted interpolation on each sub-array separately. By dividing the large-scale interpolation problem into smaller sub-array problems, the method reduces calculation complexity while maintaining improved image clarity through the weighted combination of multiple sub-array results
Solution Approach 2:
The patent merges the interpolation results from multiple pixel sub-arrays by calculating weighted averages. This combining approach improves image clarity by integrating information from different sub-arrays, while the use of pre-calculated weights and systematic combination methods helps manage the overall calculation complexity
Data Source
AI summary
The present application discloses a method of interpolating an image including a pixel array formed of multiple pixels. The method includes: determining a position of an interpolation point relative to the pixel array; determining weights of an edge direction of the pixel array at the position of the interpolation point in a plurality of preset edge directions based on pixel values of a plurality of pixels adjacent to the interpolation point; selecting, for each preset edge direction, one or more corresponding pixel sub-arrays covering the interpolation point from the pixel array to calculate an interpolation pixel value of the interpolation point; calculating a weighted interpolation pixel value of the interpolation point based on the weights of the edge direction of the pixel array at the position of the interpolation point in the preset edge directions and the interpolation pixel value of the interpolation point corresponding to each preset edge direction.