Image Sensor Noise Modeling Using Bayer Domain Signals
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Solution Overview
Problem
Conventional image sensor noise reduction techniques are limited by assuming Additive White Gaussian Noise (AWGN) and only considering shot noise, failing to adequately address various noise types, leading to degraded image quality despite noise removal processing.
Innovation Solution
A method for modeling integrated noise in image sensors using Bayer domain signals, incorporating dark-current, shot, and fixed-pattern noise models, with noise levels determined based on exposure time and pixel data, and filtered using these models to improve noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional AWGN assumption and shot noise modeling are used, then noise removal processing can be performed, but image quality remains degraded due to insufficient noise type reflection
Solution Approach 1:
The patent segments the noise modeling process by dividing noise into distinct types (dark-current noise, shot noise, fixed-pattern noise) and modeling each separately with specific characteristics. This segmentation allows accurate representation of different noise sources while maintaining computational efficiency, resolving the contradiction between noise removal effectiveness and image quality preservation.
Solution Approach 2:
The patent creates a composite noise model that integrates multiple noise types (dark-current, shot, and fixed-pattern noise) into a unified framework. This composite approach enables comprehensive noise characterization that reflects real image sensor behavior across various brightness levels, improving both noise removal effectiveness and final image quality.
2Manufacturing precision
If multiple noise types are modeled accurately, then image quality improves, but computational complexity increases
Solution Approach 1:
The patent implements dynamic noise modeling where the noise characteristics adapt based on image brightness levels. The noise models are activated or adjusted according to the detected brightness, allowing accurate multi-type noise representation in low-light conditions while using simplified models in high-light conditions. This dynamic approach maintains image quality without consistently increasing computational complexity.
Solution Approach 2:
The patent changes noise model parameters based on brightness levels and other imaging conditions. By adjusting which noise models are active and their specific parameters according to scene conditions, the system achieves accurate noise characterization when needed while reducing computational overhead during simpler conditions, thus balancing image quality and device complexity.
3Adaptability or versatility
If noise models are updated frequently to adapt to different conditions, then adaptability improves, but processing time increases
Solution Approach 1:
The patent performs preliminary characterization of noise models during system setup or calibration phases, storing noise statistics and parameters in advance. During actual image processing, the system retrieves pre-characterized noise models rather than computing them in real-time, enabling rapid adaptation to different imaging conditions without increasing processing time during the critical image analysis phase.
Solution Approach 2:
The patent creates copies of noise models that can be rapidly instantiated for different imaging conditions without requiring complex real-time computations. By using pre-computed noise model copies that can be quickly loaded and applied, the system achieves high adaptability to various brightness levels and conditions while maintaining fast processing speeds during actual image analysis.
Data Source
AI summary
A method is for processing a Bayer domain signal of an image sensor to model an integrated noise in the image sensor. The method includes receiving the Bayer domain signal of the image signal, setting a plurality of noise models using the Bayer domain signal, and determining an integrated noise level in the image sensor based on the plurality of noise models. The noise models include a dark-current noise model, a shot noise model and a fixed-pattern noise model.