Time-Resolved Imaging Through Fog Using Photon Distribution Segmentation
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Solution Overview
Problem
Conventional imaging technologies through fog, such as radar and time-gating, face challenges with poor spatial resolution, low signal-to-noise ratio, and inability to distinguish between light reflecting from fog and target objects, limiting their effectiveness in identifying objects and measuring material properties.
Innovation Solution
A system that uses a probabilistic algorithm to estimate fog parameters from time-resolved light sensor measurements, distinguishing between photons reflecting from the target and those from the fog, and computes reflectance and depth without prior knowledge of the scene depth map, utilizing all types of photons, including scattered and un-scattered, to improve resolution and signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional time gating is used to image through fog, then imaging capability is achieved, but signal-to-noise ratio is low and photons outside time gate are rejected
Solution Approach 1:
The patent converts the harmful effect of scattered photons (which were previously rejected as noise) into useful information by using their arrival time distribution characteristics to distinguish between ballistic photons (from target) and scattered photons (from fog). This allows all photons to be utilized for imaging, improving signal-to-noise ratio while maintaining imaging capability through fog.
Solution Approach 2:
The patent segments photons into different classes based on their arrival time distribution characteristics: ballistic photons (Gaussian distribution) and scattered photons (Gamma distribution). This segmentation allows the system to process different photon types differently, extracting target information from ballistic photons and fog information from scattered photons, thereby improving overall measurement precision.
2Reliability
If long integration times are used in time gating, then imaging through fog is achieved, but processing speed is reduced and prior knowledge of depth map is required
Solution Approach 1:
The patent performs preliminary classification of photons into ballistic and scattered categories based on their arrival time distributions before processing. This preliminary action enables the system to quickly identify and process only the relevant photons (ballistic for target, scattered for fog) without requiring long integration times or prior depth map knowledge, thus improving processing speed while maintaining imaging capability.
3Reliability
If radar with long wavelengths is used, then penetration through fog is achieved, but spatial resolution is poor
Solution Approach 1:
The patent changes the fundamental parameter of light interaction by using the temporal distribution parameters (arrival time statistics) of photons instead of relying solely on wavelength. By analyzing the Gaussian vs. Gamma distribution characteristics of photon arrival times, the system achieves both fog penetration and high spatial resolution, overcoming the limitation of radar's long wavelengths.
4Measurement precision
If all photons are used in the measurement, then resolution and signal-to-noise ratio are improved, but computational complexity increases
Solution Approach 1:
The patent segments photons into two distinct classes (ballistic and scattered) based on their arrival time distribution characteristics. This segmentation simplifies the computational task by allowing separate processing of each photon type: ballistic photons for target imaging and scattered photons for fog characterization. This approach improves resolution and signal-to-noise ratio while managing computational complexity through structured 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 system accurately recovers reflectance and depth of objects occluded by dense fog, enabling applications in self-driving cars, aircraft, and other vehicles to navigate through foggy conditions by providing clear images and obstacle detection.
Implementation Method 1
Light reflects back to a time-resolved light sensor from or through the fog. Some photons that arrive at the light sensor reflect from a target object which is being imaged. Other photons that arrive at the light sensor reflect from the fog itself without ever interacting with the target object.
Implementation Method 2
The probabilistic algorithm may exploit the fact that times of arrival of photons reflected from the fog itself have a distribution (Gamma) that is different than the distribution (Gaussian) of times of arrival of photons reflected from the target object.
Data Source
AI summary
A light source may illuminate a scene that is obscured by fog. Light may reflect back to a time-resolved light sensor. For instance, the light sensor may comprise avalanche photodiodes that are not single-photon sensitive. The light sensor may perform a raster scan. The imaging system may determine reflectance and depth of the fog-obscured target. The imaging system may perform a probabilistic algorithm that exploits the fact that times of arrival of photons reflected from fog have a Gamma distribution that is different than the Gaussian distribution of times of arrival of photons reflected from the target. The imaging system may adjust frame rate locally depending on local density of fog, as indicated by a local Gamma distribution determined in a prior step. The imaging system may perform one or more of spatial regularization, temporal regularization, and deblurring.