Imaging Sensor ANN Compensation for Cross-Talk and CRA Shift
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Solution Overview
Problem
Existing image sensors, particularly CMOS image sensors, face challenges in correcting multi-spectral imaging due to cross-talk and chief ray angle (CRA) shift, which affect pixel spectral response and make image data correction difficult.
Innovation Solution
An image processing unit equipped with an artificial neural network (ANN) is used to predict and correct pixel errors by training on flat-field and real-world images, utilizing a spatially dependent convolutional neural network (CNN) to address cross-talk and CRA components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing corrections are applied to multi-spectral imaging data, then some basic corrections can be performed, but the corrections are insufficient due to complex cross-talk and CRA shift effects
Solution Approach 1:
The patent replaces traditional mechanical/optical correction methods with an artificial neural network-based computational approach. The ANN model processes image data and predicts correction values for cross-talk and CRA shift effects, substituting complex physical correction mechanisms with intelligent algorithms that can handle non-linear optical effects more effectively.
Solution Approach 2:
The patent introduces an artificial neural network as an intermediary between the image sensor and the final image output. This intermediary component processes the raw image data, predicts the effects of cross-talk and CRA shift, and generates corrected image data, thereby mediating the complex correction process and improving overall correction accuracy.
2Measurement precision
If the spectral response of pixels is corrected for CRA shift, then spectral accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the artificial neural network on a comprehensive dataset of image data with known spectral characteristics and correction values. During inference, the pre-trained model can quickly predict correction values without requiring time-consuming real-time calculations, thus reducing processing time while maintaining high spectral accuracy.
Solution Approach 2:
The patent utilizes parameter changes by transforming the correction process from pixel-by-pixel physical parameter adjustment to a data-driven prediction approach. The ANN model learns the relationship between input image parameters and correction values, enabling fast prediction through pattern recognition rather than exhaustive computational search, thereby reducing processing time.
3Reliability
If cross-talk effects are corrected in multi-spectral imaging, then image quality improves, but the difficulty of detecting and measuring correction effectiveness increases
Solution Approach 1:
The patent implements feedback mechanisms where the system evaluates the corrected image data against expected spectral characteristics and provides feedback on correction effectiveness. This feedback loop enables the system to measure whether cross-talk corrections are successful by comparing predicted versus actual spectral responses, thereby making correction effectiveness measurable and monitorable.
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
The ANN effectively corrects image artifacts caused by cross-talk and CRA shift, improving image quality by spatially varying pixel responses.
Implementation Method 1
an image signal processor configured to operate the ANN on an image output by the image sensor to predict an error value of a pixel of the image
Implementation Method 2
Image sensors generate an image of an object using a photoelectric conversion element, which reacts to the intensity of light reflected from the object
Data Source
AI summary
An image processing unit includes an imaging sensor having a pixel array, a memory storing an artificial neural network (ANN), and an image signal processor configured to operate the ANN on an image output by the imaging sensor to predict an error value of a pixel of the image and correct the pixel using the predicted error value.