Camera Image Correction Using Tangential and Sagittal Blur Data
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Solution Overview
Problem
Existing image correction methods do not account for individual camera characteristics, leading to ineffective correction of image distortions caused by tangential and sagittal image blurs, resulting in low image resolution.
Innovation Solution
An image correction apparatus and method that involves obtaining and applying tangential and sagittal image blur correction data, including modulation transfer function plots, to correct image distortions by considering the specific characteristics of each camera, using a chart with regions of interest in both directions to generate and store correction data for subsequent image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If uniform image correction is applied without considering individual camera characteristics, then the correction process is simple and fast, but the correction effectiveness is poor and image resolution is lowered
Solution Approach 1:
The patent performs preliminary measurement of tangential and sagittal image blur characteristics using a chart with circles during camera manufacturing or calibration. This preliminary action captures individual camera characteristics and stores them as correction data, which is then applied to subsequent images. By performing the complex measurement and data collection upfront, the actual image correction process becomes simpler and faster while maintaining high effectiveness tailored to each camera's specific characteristics.
Solution Approach 2:
The patent applies different correction characteristics to different regions of the image based on measured blur data. Specifically, it separately measures and corrects tangential and sagittal blur components, applying localized correction strategies to different areas of the image where different blur characteristics exist. This local quality approach ensures optimal correction for each region while maintaining overall simplicity through automated processing.
2Reliability
If individual camera characteristics are considered for image correction, then correction effectiveness is improved, but measurement and processing time increase
Solution Approach 1:
The patent performs time-consuming measurements of individual camera characteristics using a chart with circles during the manufacturing or calibration phase. This preliminary action captures all necessary blur data once, and the results are stored for rapid retrieval and application during actual image processing. By moving the time-consuming measurement to the preliminary phase, subsequent image correction operations become much faster while maintaining individual camera optimization.
Solution Approach 2:
The patent creates a copy of the camera's specific blur characteristics by measuring them against a standard chart and storing the results as correction data. This copied information (the chart-based measurement data) can then be rapidly applied to multiple images without re-measuring the camera characteristics each time. The copying process allows the complex individual characterization to be replicated efficiently across multiple images.
3Reliability
If tangential and sagittal blur correction is performed, then image clarity is improved, but the correction algorithm becomes more complex
Solution Approach 1:
The patent segments the image correction problem into separate tangential and sagittal components. By measuring and correcting these two blur directions independently using distinct algorithms and parameters, the overall complexity is managed through modularization. Each component can be optimized separately, and the results are combined to achieve comprehensive clarity improvement without creating an intractably complex unified algorithm.
Data Source
AI summary
Disclosed is an image correction method, implemented in an image correction apparatus that corrects an image taken by a camera, the method including: obtaining a first image by taking an image of a chart including a plurality of circles, each of which includes one or more regions of interest (ROIs) in a tangential direction and a sagittal direction, through the camera; selecting and storing tangential and sagittal image blur correction data of the camera, based on image blur data in the tangential and sagittal directions of the camera measured using the obtained first image; and loading the stored tangential and sagittal image blur correction data and applying the loaded tangential and sagittal image blur correction data to correction for a second image taken by the camera. Thus, image distortion due to, in particular, tangential and sagittal image blurs is effectively corrected by taking individual characteristics of the camera into account.