CCD Color Noise Estimation Using Temperature and Gain
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Solution Overview
Problem
Existing image pickup systems struggle to accurately reduce color noise due to dynamic variations in noise levels caused by factors like temperature and exposure settings, leading to poor image quality and signal deterioration.
Innovation Solution
An image pickup system and program that calculate luminance and color difference signals, estimate color noise amounts in predetermined regions, and reduce noise based on these estimates, using dynamic parameters like temperature and gain to optimize noise reduction while preserving original signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static constant terms are used to formulate noise amount, then the formulation is simple, but the estimation accuracy is poor because luminance noise varies dynamically with temperature, exposure time, and gain
Solution Approach 1:
The patent applies dynamics by transitioning from static constant terms to dynamic parameters that change with shooting conditions. The noise amount formulation now incorporates temperature, exposure time, and gain as variable parameters, allowing the system to adapt to changing environmental and operational conditions, thereby improving estimation accuracy without excessive complexity
Solution Approach 2:
The patent implements parameter changes by modifying the noise amount formulation to include dynamic parameters (temperature, exposure time, gain) instead of static constants. This allows the system to adjust noise estimation according to actual shooting conditions, resolving the contradiction between simplicity and accuracy
2Reliability
If uniform filtering is applied without distinguishing regions, then the processing is simple, but edge deterioration occurs in regions with large estimated noise amounts
Solution Approach 1:
The patent applies local quality by differentiating processing approaches for different image regions. Edge regions are identified and processed differently from flat regions, with edge detection mechanisms preventing noise reduction in areas where it would cause deterioration. This localized approach maintains signal quality while avoiding the pitfalls of uniform filtering
Solution Approach 2:
The patent segments the image processing into distinct regions (edge regions and flat regions) and applies appropriate noise reduction strategies to each. This segmentation allows the system to preserve edges while reducing noise in suitable areas, improving overall signal quality without requiring overly complex global processing
3Measurement precision
If fixed smoothing is applied to color difference signals, then the processing is simple, but optimal noise reduction cannot be achieved because color noise varies with signal levels
Solution Approach 1:
The patent applies dynamics to the smoothing process by making it adaptive to color noise characteristics. The smoothing strength is dynamically adjusted based on local color noise amounts and signal levels, allowing optimal noise reduction in different regions while preserving important image details. This dynamic approach resolves the contradiction between simple fixed smoothing and accurate adaptive smoothing
Data Source
AI summary
An image pickup system includes a color noise estimation section, which has a local region extraction section that extracts luminance and color difference signals in units of 4×4 pixels from signals of a CCD with a color filter, an average luminance calculation section that calculates an average luminance value from the luminance signals, a temperature estimation section that estimates a temperature T of the CCD from a signal of an OB region, a gain calculation section that calculates a gain G of the signals based on information from a control section, a coefficient and a function calculation sections for estimating a color noise amount from the average luminance value based on a formulation using the temperature and the gain, and a color noise reducing section that reduces color noise in the color difference signals in units of 4×4 pixels based on the estimated color noise amount.