Automotive Camera Noise Filtering Texture Preservation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional High Dynamic Range (HDR) automotive camera imagers suffer from texture loss at bright light conditions due to suboptimal noise filtering, which results in increased noise and a non-continuous Signal to Noise Ratio (SNR), leading to poor image quality and reduced texture preservation.
Innovation Solution
A testing system that processes multiple frames of image data with different register settings to measure the signal-to-noise ratio and texture Key Performance Indicator (KPI), optimizing the camera's noise filtering to find a 'sweet spot' that balances noise reduction and texture preservation, using methods such as the integral of Modulation Transfer Function (MTF) power between 1/(2.5*pixel size and 1/(5*pixel size) as a KPI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional HDR noise filtering is applied, then noise is reduced, but texture preservation deteriorates
Solution Approach 1:
The patent implements dynamic register settings that adjust noise filtering strength based on local image characteristics and lighting conditions. The system transitions from static to dynamic control, allowing the camera to adaptively balance noise reduction and texture preservation across different regions and conditions, resolving the contradiction by making the filtering process context-dependent rather than uniform.
Solution Approach 2:
The patent changes the parameters of noise filtering by introducing multiple register settings with different filtering strengths. By adjusting these parameters dynamically based on measured SNR and texture KPI values, the system optimizes the balance between noise reduction and texture preservation, transforming the fixed-parameter conventional approach into a variable-parameter adaptive system.
2Object-affected harmful factors
If noise filtering is increased, then noise is reduced, but image quality deteriorates due to texture loss
Solution Approach 1:
The patent implements a feedback mechanism that measures both SNR and texture KPI values, then uses these measurements to adjust register settings. This closed-loop control ensures that noise filtering is optimized without compromising image quality, as the system continuously monitors and responds to actual image characteristics rather than applying fixed filtering regardless of outcome.
Solution Approach 2:
The system adjusts filtering parameters based on measured performance metrics, changing the strength of noise filtering dynamically. When texture preservation metrics indicate degradation, the system reduces filtering strength; when noise levels are problematic, it increases filtering. This parameter adaptation resolves the contradiction by making filtering intensity conditional on actual image quality outcomes.
3Object-affected harmful factors
If register settings are optimized for noise reduction, then noise is reduced, but texture preservation worsens
Solution Approach 1:
The patent introduces multiple register settings with different parameter configurations for noise filtering. By selecting and adjusting these parameters based on measured SNR and texture KPI values, the system optimizes the balance between noise reduction and texture information preservation, transforming single-parameter optimization into multi-parameter balanced optimization.
Solution Approach 2:
The system dynamically selects among different register settings based on real-time image characteristics and measured performance metrics. This dynamic selection allows the camera to adapt to varying lighting conditions and scene content, preserving texture information when important while reducing noise when possible, thereby resolving the information loss contradiction.
Data Source
AI summary
A method of testing a camera for vision system for a vehicle includes providing a camera configured for mounting and use on a vehicle. The camera is operable at selected ones of a plurality of register settings. A test pattern is disposed in the field of view of the camera and at least two frames of image data are captured with the camera using different register settings having noise filtering at a respective one of at least two levels between a maximum noise filtering and a minimum noise filtering. The signal to noise ratio is measured for each of the at least two frames of captured image data. A texture value is measured for each of the at least two frames of captured image data. A compromise is selected between noise reduction and texture preservation.