Inverse Transform Filter for Image Aberration Correction
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Solution Overview
Problem
Existing image capturing apparatuses face challenges in restoring optical aberration while suppressing noise, leading to a trade-off between resolution and noise levels, where improving one aspect often worsens the other.
Innovation Solution
An image capturing apparatus comprising an optical system that introduces aberration, an image capturing unit, and an inverse transform unit that applies a specific inverse transform filter to correct aberration while minimizing noise amplification, using a combination of Fourier and inverse Fourier transforms to derive optimal filters for each image area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a noise reduction process is applied to reduce noise in the captured image, then the noise level decreases, but the resolution deteriorates due to blur
Solution Approach 1:
The image is divided into multiple local regions, and each region is processed independently with region-specific inverse transform filters. This segmentation allows the restoration process to adapt to local characteristics, reducing noise while preserving resolution in different areas of the image.
Solution Approach 2:
Different inverse transform filters are applied to different local regions of the image based on their specific characteristics. This local quality approach ensures that each region receives optimized processing, maintaining resolution while reducing noise according to local image properties.
2Manufacturing precision
If an inverse transformation process is applied to restore blur and improve resolution, then the image resolution improves, but the noise amount increases due to amplification
Solution Approach 1:
The inverse transform filters are designed with adjustable parameters that control the balance between resolution restoration and noise suppression. By optimizing these filter parameters, the system achieves resolution improvement while minimizing noise amplification.
Solution Approach 2:
The filter design incorporates feedback mechanisms where the filter characteristics are optimized based on the relationship between resolution restoration and noise suppression. This feedback loop allows the system to find the optimal balance point.
3Device complexity
If a single inverse transform filter is applied to the entire image, then the processing is simple, but the restoration accuracy deteriorates due to inability to adapt to local variations
Solution Approach 1:
The image is segmented into multiple local regions, each processed with its own inverse transform filter. This segmentation improves restoration accuracy by adapting to local variations while keeping the overall processing framework relatively simple.
Solution Approach 2:
Instead of applying a single filter to the entire image, the system applies multiple filters to different parts of the image. This partial action approach improves accuracy without requiring excessive computational complexity.
Data Source
AI summary
An image capturing apparatus includes: an optical system that gives aberration to incident light; an image capturing unit that converts the light that has passed through the optical system into pixels, and captures an image; and an inverse transform unit that obtains a first inverse transform filter for restoring the aberration for each predetermined part of the captured image captured by the image capturing unit, and performs a first inverse transformation process on the captured image by the first inverse transform filter.