Dynamic Noise Estimation for Image Pickup Systems
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Solution Overview
Problem
Existing image pickup systems struggle to effectively reduce random noise due to the use of static constant terms, which leads to inferior noise estimation and deterioration of original signals, especially in varying shooting conditions and subject types, resulting in suboptimal noise reduction and image quality.
Innovation Solution
An image pickup system comprising a noise estimating unit and a shooting situation estimating unit that dynamically corrects noise estimates based on shooting conditions, allowing for precise noise reduction tailored to specific image areas and subject types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static constant terms are used for noise estimation, then the device complexity is reduced, but the measurement precision of noise amount deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static constant terms to dynamic parameter estimation. The system now estimates noise amount parameters a, b, and c based on actual shooting conditions (temperature, exposure time, gain) rather than using fixed predetermined values. This allows the noise estimation to adapt to varying shooting conditions, improving precision while maintaining reasonable system complexity through efficient parameter calculation methods.
Solution Approach 2:
The patent implements parameter changes by making the noise estimation parameters (a, b, c) variable based on shooting conditions. Instead of using fixed constant terms, the system calculates these parameters dynamically according to temperature, exposure time, and gain settings. This parameter adaptation resolves the contradiction by improving measurement precision through condition-specific estimation while managing device complexity through systematic parameter calculation.
2Productivity
If filtering frequency characteristics are controlled based on signal level, then the productivity of noise reduction is improved, but the manufacturing precision of signal preservation deteriorates
Solution Approach 1:
The patent applies local quality by implementing region-specific noise reduction processing. The system divides the image into different regions (flat portions and edge portions) and applies appropriate filtering characteristics to each region based on its local characteristics. This resolves the contradiction by improving overall noise reduction efficiency through targeted processing while maintaining high signal preservation precision in edge regions through specialized filtering approaches.
Solution Approach 2:
The patent implements segmentation by separating the image processing into distinct regions (flat areas and edge areas) with different processing strategies. The system identifies edge portions and applies different filtering frequency characteristics compared to flat portions, allowing efficient noise reduction in uniform areas while preserving signal integrity in complex regions. This segmentation approach improves productivity without compromising signal preservation precision.
3Ease of operation
If threshold-based moving average method is used, then the ease of operation is improved, but the measurement precision of noise reduction control deteriorates
Solution Approach 1:
The patent implements parameter changes by replacing the fixed threshold value with dynamically estimated noise amount parameters. Instead of comparing against a predetermined threshold, the system uses calculated parameters (a, b, c) that reflect actual shooting conditions to determine the optimal number of pixels for moving average processing. This improves measurement precision while maintaining ease of operation through automated parameter-based control.
Solution Approach 2:
The patent applies feedback by using the estimated noise amount parameters to continuously adjust the moving average processing. The system calculates noise parameters based on shooting conditions and uses these parameters to control the filtering strength and pixel selection. This feedback mechanism improves noise reduction precision by adapting to actual conditions while keeping the operation simple through automated parameter-driven control.
Data Source
AI summary
An image pickup system has a noise estimating unit which estimates the amount of noise contained in a digitized signal from an image pickup element composed of an array of a plurality of pixels, either for each pixel or for each specified unit area made up of a plurality of pixels, and a shooting conditions estimation unit which estimates the shooting condition when an image based on the signal is acquired. The amount of noise estimated by the noise estimating unit is corrected on the basis of the shooting conditions estimated by the shooting conditions estimation unit, and the noise in the signal is reduced on the basis of the corrected amount of noise.