Uncooled Solid-State Image Sensor Noise Reduction
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Solution Overview
Problem
Current night vision technologies face limitations in image quality and dynamic range due to noise in low light conditions, where the small number of photons results in degraded images, and cooling methods to reduce noise require significant power, making battery operation challenging.
Innovation Solution
An uncooled solid-state low light image sensor combined with energy-efficient image processing algorithms and a high-performance microcoded multicore microprocessor architecture to enhance image quality, capable of performing necessary processing at low energy consumption for video imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cooling methods are used to reduce thermal noise in low light sensors, then sensor performance is improved, but power consumption increases significantly
Solution Approach 1:
The patent changes the operational parameters of the image sensor by using uncooled operation with modified readout techniques and signal processing algorithms. Instead of cooling the sensor to reduce thermal noise, the system adjusts readout timing, gain settings, and applies computational noise reduction to achieve acceptable performance without the high power consumption of active cooling systems.
Solution Approach 2:
The patent replaces the mechanical/thermal cooling system with a computational approach. Rather than physically removing heat from the sensor, the system uses digital signal processing and image processing algorithms to identify and reduce noise artifacts in the captured images, substituting a thermal management system with an information processing system.
2Use of energy by moving object
If uncooled solid-state sensors are used to reduce power consumption, then battery operation becomes feasible, but image quality and dynamic range are degraded due to noise
Solution Approach 1:
The patent introduces an intermediary processing stage between the uncooled sensor and the final image output. Multiple frames are captured and processed through computational algorithms that act as an intermediary to separate signal from noise, enhancing image quality without requiring the sensor itself to be cooled.
Solution Approach 2:
The patent performs preliminary actions by capturing multiple frames before final image generation. These preliminary frames are processed to extract the true signal while canceling out random noise, allowing the system to achieve high image quality from an uncooled sensor that would otherwise produce noisy single-frame images.
3Measurement precision
If image processing algorithms are applied to enhance image quality from noisy sensors, then image quality is improved, but computing capacity requirements increase
Solution Approach 1:
The patent segments the image processing task into multiple independent frames that are processed separately before being combined. This segmentation allows the computational workload to be distributed and optimized, reducing the peak computing capacity required while still achieving the noise reduction benefits of multi-frame processing.
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 solution generates high-quality images with improved dynamic range and reduced noise, enabling effective night vision in low light conditions without the power consumption issues of cooling methods, enhancing situational awareness and operational effectiveness.
Implementation Method 1
When a photon arrives, a photo-electron may be generated
Implementation Method 2
The photo-electron is directed by a bias voltage toward the avalanche photodiode junction, where it is accelerated by the high static electric field in the junction. The high velocity electron collides with atoms in the junction region, causing impact ionization action that generates a burst of approximately 100-200 additional electrons
Implementation Method 3
It is generally recognized that low light image sensing is greatly affected by noise. The reason for this is that in the lowest light conditions contemplated for night vision applications such as overcast starlight, the number of discrete photons arriving at each pixel in a video image sensor during the frame integration time may be very low
Data Source
AI summary
An imaging system may include: at least one photon sensing pixel; at least one digital counting circuit; and at least one processing core programmed to apply at least one image processing algorithm to at least one pixel sample of the at least one digital counting circuit.