2D SNR Mapping for Sensor Images Under Variable Lighting
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Solution Overview
Problem
Current sensor-based systems in autonomous vehicles and advanced driver-assistance systems face challenges in achieving high signal-to-noise ratio (SNR) and dynamic range, which affects the detection of road users and objects, leading to suboptimal performance in various driving conditions.
Innovation Solution
The implementation of a 2D SNR map generation process using a pixel value to SNR value lookup table (LUT) that adjusts sensor operating parameters based on noise models and environmental conditions, allowing for improved image quality and noise level prediction across the scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor operating parameters are fixed, then device complexity is reduced, but image quality and SNR performance deteriorate under varying environmental conditions
Solution Approach 1:
The patent implements dynamic adjustment of sensor operating parameters (integration time, gain settings) based on real-time environmental conditions detected by the sensor system. The processor continuously monitors scene characteristics and modifies sensor parameters accordingly, transforming a static sensor system into a dynamic one that adapts to varying lighting and environmental conditions, thereby maintaining high SNR performance without requiring complex manual intervention
Solution Approach 2:
The sensor system performs self-optimization by automatically detecting environmental conditions and adjusting its own operating parameters without external control. The processor analyzes the captured images and sensor data to determine optimal integration times and gain settings, enabling the system to self-regulate and maintain optimal SNR performance across different driving conditions
2Reliability
If sensor integration time is increased to improve SNR, then signal quality improves, but frame rate and temporal resolution deteriorate
Solution Approach 1:
The system dynamically adjusts integration time based on real-time scene analysis rather than using a fixed integration time. In low-light conditions, longer integration times are used to improve SNR, while in bright conditions or when motion is detected, shorter integration times maintain adequate signal quality while preserving higher frame rates. This dynamic adjustment resolves the trade-off between signal quality and temporal resolution
Solution Approach 2:
The patent changes the integration time parameter adaptively based on environmental conditions and scene characteristics. The processor calculates optimal integration times by analyzing noise levels, signal strength, and scene content, then adjusts the integration time parameter accordingly. This parameter change strategy allows the system to optimize SNR when needed while maintaining acceptable frame rates under different conditions
3Reliability
If sensor gain is increased to improve signal amplification, then SNR improves, but noise amplification and dynamic range deteriorate
Solution Approach 1:
The system dynamically adjusts gain settings based on real-time signal level detection. When signals are weak, the gain is increased to amplify the signal, but when signals are strong or noise levels rise, the gain is reduced to prevent noise amplification and maintain dynamic range. This dynamic gain control allows the system to optimize signal amplification while minimizing harmful noise effects under varying conditions
Solution Approach 2:
The patent implements adaptive gain parameter changes based on scene brightness and noise characteristics. The processor monitors the captured images and sensor output levels, then adjusts the gain parameter to maintain optimal signal amplification without excessive noise. This parameter adjustment strategy balances signal enhancement with noise control by changing gain levels according to environmental conditions
4Measurement precision
If multiple sensor parameters are adjusted to optimize image quality, then detection accuracy improves, but processing complexity and computational load increase
Solution Approach 1:
The patent applies different parameter optimization strategies to different regions of the scene based on local characteristics. The processor identifies regions of interest and applies targeted parameter adjustments (integration time, gain) specifically to those regions rather than uniformly adjusting all parameters across the entire image. This local optimization approach maintains high detection accuracy while reducing overall processing complexity by focusing computational resources where they are most needed
Data Source
AI summary
Techniques are disclosed for generating a two-dimensional (2D) map of signal-to-noise ratio (SNR) values for sensor-acquired images. The techniques leverage the use of lookup tables (LUTs) to generate a transformation LUT that functions to map pixel values to SNR values. The transformation LUT may be generated by first generating an intermediate LUT that uses the operating parameters identified with the sensor to map pixel values to light level values. The light level values are then used together with an SNR model that outputs a prediction of electrons identified with a signal portion and a noise portion of images acquired by the sensor to thus map the pixel values to SNR values. The 2D map may be used to improve upon the accuracy of the classification of objects and/or scene characteristics for various applications.