Anisotropic Spatial Filter Image Processing Method
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Solution Overview
Problem
Conventional image processing techniques fail to effectively address anisotropy in image resolution, leading to degradation and Moiré patterns when spatial filters are limited to a predetermined number of elements, resulting in reduced image quality.
Innovation Solution
An image processing method that calculates a second spatial filter with more elements than the blur size and generates multiple spatial filters with a predetermined number of elements or less from the second spatial filter, using a finite filter with a total sum of zero and at least two non-zero elements, to improve anisotropy in resolution and prevent image degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a finite spatial filter with a predetermined number of elements is used, then the device complexity is reduced, but the manufacturing precision of resolution deteriorates due to anisotropy
Solution Approach 1:
The image processing is divided into multiple stages: first applying a finite spatial filter with predetermined elements to reduce complexity, then applying a second spatial filter with larger number of elements to correct resolution precision. This segmentation allows each filter to have optimized element counts for their specific functions.
Solution Approach 2:
The finite spatial filter is applied first as a preliminary step to reduce computational complexity and prepare the image data, followed by the second spatial filter that addresses the resolution precision. This preliminary action enables the subsequent filter to work more effectively with reduced data complexity.
2Productivity
If a finite spatial filter with a predetermined number of elements is used, then the productivity is improved, but the manufacturing precision of resolution deteriorates due to anisotropy
Solution Approach 1:
The processing is segmented into two sequential stages with different filter complexities. The first stage uses a compact finite filter for rapid processing, while the second stage uses a larger filter to correct resolution precision, achieving both high productivity and precision.
Solution Approach 2:
The first spatial filter performs a partial correction with limited elements to maintain productivity, while the second spatial filter applies excessive correction with more elements to ensure resolution precision is achieved in the final output.
3Manufacturing precision
If different filtering is performed according to position to correct blur, then the manufacturing precision of resolution is improved, but the anisotropy in resolution cannot be improved
Solution Approach 1:
The patent applies different filtering characteristics to different spatial frequencies and directions. The first spatial filter targets specific frequency ranges while the second spatial filter addresses directional anisotropy, creating locally optimized correction for different image regions and orientations.
Solution Approach 2:
The patent changes the parameters of the spatial filters between stages - the first filter has specific element values optimized for initial correction, while the second filter has different element values optimized for correcting anisotropy. This parameter transformation enables both precision improvement and uniformity restoration.
Data Source
AI summary
An information processing method includes calculating a second spatial filter having a size of the number of elements larger than a blur size of an image using a finite first spatial filter having an anisotropy in resolution of the image and a finite filter in which a value of a total sum of elements is zero and at least two elements have a non-zero value, and generating a plurality of spatial filters having a predetermined number of elements or less from the second spatial filter.