Photosensor Dark Pixel Offset Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensor calibration methods for photosensors face challenges in efficiently correcting for temperature-dependent and gain-dependent dark current noise, particularly in low-power applications where temperature controllers are not used, leading to suboptimal image quality due to increased memory and processing requirements.
Innovation Solution
A method and system that model dark current correction using global scale and bias factors dependent on temperature and gain, along with a temperature-independent offset for each pixel, reducing memory and processing needs by using lookup tables and interpolation/extrapolation methods, allowing for efficient correction of raw pixel values in photosensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dark current correction is performed using pixel-dependent parameters for each temperature and gain setting, then correction accuracy is improved, but memory and processing requirements increase
Solution Approach 1:
The correction parameters are segmented into two components: a global scale and bias factor that applies to all pixels (capturing temperature and gain dependence), and a pixel-specific offset term (capturing individual pixel characteristics). This segmentation allows the system to maintain correction accuracy while reducing memory requirements by not storing full per-pixel per-temperature-per-gain correction tables.
Solution Approach 2:
The patent changes the parameter representation from storing complete correction tables (requiring significant memory) to storing only scale, bias, and offset parameters. By using interpolation and extrapolation methods, the system can compute correction values for any temperature or gain setting from these minimal parameters, dramatically reducing memory requirements while maintaining correction accuracy.
2Object-generated harmful factors
If temperature controllers are used to maintain stable sensor temperature, then dark current noise is reduced, but power consumption increases
Solution Approach 1:
The patent replaces the mechanical/thermal control system (temperature controllers) with a computational approach. Instead of actively controlling temperature to stabilize dark current, the system uses mathematical models (scale, bias, and offset parameters with interpolation/extrapolation) to correct for temperature-dependent dark current effects in software, eliminating the need for power-consuming temperature control hardware.
Solution Approach 2:
The system changes from controlling the physical parameter (temperature) to controlling the computational parameters (scale, bias, offset values that model temperature dependence). By storing and using these correction parameters, the system can compensate for temperature variations without actually controlling the temperature, thereby reducing power consumption while maintaining image quality.
3Measurement precision
If complex calibration models are used to account for temperature and gain dependencies, then correction accuracy is improved, but processing speed decreases
Solution Approach 1:
The complex calibration model is segmented into simple, computationally efficient components: global scale and bias factors plus pixel-specific offset terms. This segmentation allows the correction to be performed using simple arithmetic operations (multiplication by scale, addition of bias and offset) rather than complex calculations, maintaining high processing speed while achieving accurate correction through the clever parameter structure.
Solution Approach 2:
The patent transforms the complex calibration problem into a simple parameter application problem. By pre-computing scale, bias, and offset parameters during calibration and storing them for later use, the actual image correction process requires only simple parameter application and interpolation operations, dramatically improving processing speed compared to running complex calibration algorithms in real-time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces memory and processing requirements, enhancing processing speed and efficiency while maintaining accurate dark current correction, suitable for low-power applications and sensors without temperature controllers.
Implementation Method 1
photosensors include, for example, devices such as charge-coupled devices (CCDs) in which each pixel includes a photoactive capacitor, and active pixel image sensors in which each pixel includes a light sensor and an active amplifier
Implementation Method 2
even when no light is incident on the pixel, a small amount of electric current, commonly called 'dark current,' can flow from the pixel
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Examples of systems and methods to provide estimates of dark current for pixels of a photosensor as a function of the temperature of the sensor and the gain applied to the photosensor are described. In various implementations, the dark current estimated for each pixel can depend at least partly on a global scale factor and a global bias that depend on temperature and gain and a temperature-independent and gain-independent offset value for each pixel. The scale, bias, and offsets may be determined from multiple dark field images taken by the sensor over a range of operating temperatures. In some cases, the scale and bias can be determined using a subset of less than all the image pixels. Scale and bias derived for a particular sensor can be used in the calibration of different sensors.