Adaptive Kernel Denoising for ToF Phase Unwrapping
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Solution Overview
Problem
Current phase unwrapping methods in time-of-flight (ToF) imaging systems face challenges in accurately determining distances due to noise, especially in areas with rapid transitions and varying lighting intensities, leading to distance ambiguities and difficulties in preserving high-frequency details.
Innovation Solution
The method involves adaptive denoising and phase unwrapping in the complex domain using variable kernel sizes based on brightness levels, with larger kernels for low brightness areas to increase signal-to-noise ratio and smaller kernels for high brightness areas to preserve high-frequency details, along with edge-preserving weighting factors to mitigate noise and enhance edge preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed-size kernel is used for denoising in phase unwrapping, then the processing is computationally efficient, but noise cannot be effectively reduced in low brightness areas and high-frequency details are lost in high brightness areas
Solution Approach 1:
The patent applies dynamics by making the kernel size adaptive rather than fixed. The kernel size dynamically adjusts based on local signal characteristics (brightness level and signal-to-noise ratio), allowing the system to optimize noise reduction and detail preservation for each region independently. This resolves the contradiction by enabling high measurement precision in low brightness areas through larger kernels while maintaining computational efficiency through smaller kernels in high brightness areas.
Solution Approach 2:
The patent implements local quality by applying different kernel sizes to different regions of the image based on local signal characteristics. Low brightness areas receive larger kernels for enhanced noise reduction, while high brightness areas receive smaller kernels to preserve high-frequency details. This localized adaptation resolves the contradiction by optimizing processing for each region's specific requirements rather than using a uniform approach.
2Reliability
If a larger kernel is used for denoising, then noise reduction is improved in low brightness areas, but high-frequency details are lost
Solution Approach 1:
The patent uses dynamics by making the kernel size adaptive based on local signal characteristics. The system dynamically selects larger kernels for low brightness areas to improve signal-to-noise ratio, while automatically using smaller kernels for high brightness areas to preserve high-frequency details. This dynamic adaptation resolves the contradiction by adjusting kernel size according to regional needs rather than applying a fixed size throughout.
Solution Approach 2:
The patent applies local quality by tailoring the kernel size to the specific characteristics of each image region. Low brightness areas with poor signal-to-noise ratio receive larger kernels for effective denoising, while high brightness areas with good signal quality receive smaller kernels to maintain detail fidelity. This localized approach resolves the contradiction by optimizing each region's processing parameters to its specific requirements.
3Measurement precision
If adaptive kernel sizes are used based on brightness levels, then noise reduction and detail preservation are optimized, but computational complexity increases
Solution Approach 1:
The patent applies dynamics by making kernel sizes adaptive rather than fixed. The system dynamically determines appropriate kernel sizes based on local brightness levels and signal-to-noise ratios, optimizing measurement precision for each region. While this increases computational complexity compared to fixed kernels, the adaptive approach ensures high accuracy in challenging low brightness areas where fixed kernels would fail, justifying the additional computational cost.
4Productivity
If phase unwrapping is performed without adaptive denoising, then processing is faster, but unwrapping errors increase in noisy areas
Solution Approach 1:
The patent applies preliminary action by performing adaptive denoising before phase unwrapping. The system first applies appropriate denoising filters with adaptive kernel sizes to reduce noise in the phase data, then proceeds with phase unwrapping. This preliminary noise reduction step prevents unwrapping errors in noisy areas while maintaining reasonable processing speed, as the denoising operation is optimized with adaptive kernels that avoid excessive computation in low-noise regions.
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 effectively reduces noise and improves the accuracy of distance measurements by adapting kernel sizes to brightness levels, resulting in sharper features and reduced unwrapping errors, while also optimizing computational efficiency by using larger kernels only where needed.
Implementation Method 1
acquiring, via an image sensor comprising a plurality of pixels, a plurality of image frames capturing light emitted from the light source that is reflected by the object
Implementation Method 2
applying an adaptive denoising process by setting a kernel size based on the brightness level
Implementation Method 3
The distance to a point on an imaged surface in the environment is determined based on the length of the time interval in which light emitted by the imaging system travels out to that point and then returns back to a sensor array in the imaging system
Data Source
AI summary
Examples are disclosed herein relating to signal processing in a time of flight (ToF) system. One example provides, a method comprising emitting, via a light source, amplitude-modulated light toward an object, acquiring, via an image sensor comprising a plurality of pixels, a plurality of image frames capturing light emitted from the light source that is reflected by the object, wherein the plurality of image frames are acquired at two or more different frequencies of the amplitude-modulated light and collectively form a multifrequency frame, and for each pixel of the multifrequency frame, determining a brightness level, applying an adaptive denoising process by setting a kernel size based on the brightness level, and performing a phase unwrapping process to determine a depth value for the pixel.