ToF Sensor Motion Artifact Handling via Cross-Correlation Guidance
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Solution Overview
Problem
Existing Time-of-Flight (ToF) sensor technologies face challenges in handling motion artifacts and noise removal, particularly in dynamic scenes, which affect accurate depth measurements and real-time processing requirements for applications like gesture recognition and head pose estimation.
Innovation Solution
A method and system that calculate cross-correlation values from sent and received signals to derive a depth map and guidance image, using a guided filter for edge-preserving noise reduction and plausibility mapping to address motion artifacts and noise, enabling real-time motion artifact handling and noise removal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ToF processing is used, then depth measurement is obtained, but motion artifacts corrupt depth measurements at moving object boundaries
Solution Approach 1:
The patent segments the image processing into distinct modules: motion artifact detection, guidance image generation, and depth map refinement. By dividing the processing pipeline, each module can address specific aspects of motion artifact removal without interfering with others, enabling precise depth measurement while handling motion corruption at object boundaries
Solution Approach 2:
The patent introduces a guidance image as an intermediary element that contains motion information and edge details. This guidance image acts as a mediator between the raw depth map and the final corrected depth map, allowing motion artifacts to be removed while preserving edges through the guided filter process
2Reliability
If optical flow calculations are performed to compensate motion, then motion artifacts are reduced, but processing time increases making real-time application non-practical
Solution Approach 1:
The patent extracts the essential motion compensation function from complex optical flow calculations. Instead of computing full optical flow fields, the method extracts motion information by analyzing phase-shifted image differences and uses this extracted information to guide the depth map correction, significantly reducing processing time while maintaining reliability
Solution Approach 2:
The patent changes the processing parameters by using a guided filter that iteratively refines the depth map based on a guidance image. This parameter-based approach replaces computationally intensive optical flow calculations with a more efficient filtering process that achieves motion artifact compensation in real-time
3Measurement precision
If noise removal is applied to depth maps, then signal quality improves, but edges may be blurred
Solution Approach 1:
The patent applies local quality by using a guided filter that adapts its behavior based on local image characteristics. The filter preserves edges in regions where the guidance image indicates strong edges, while applying noise removal in flat regions. This localized approach ensures that noise is removed without blurring important edge structures
Solution Approach 2:
The guidance image serves as an intermediary that contains edge information and motion data. The guided filter uses this intermediary to distinguish between regions that need noise removal and regions that need edge preservation, achieving both signal quality improvement and edge sharpness maintenance
4Measurement precision
If integration time is increased to improve depth accuracy, then measurement precision improves, but motion artifacts increase
Solution Approach 1:
The patent applies preliminary action by processing the depth map and guidance image in a pipeline where motion artifact removal is performed before final depth measurement. The guided filter processes the depth map using the guidance image to correct motion artifacts, allowing the system to use longer integration times for accuracy while removing motion corruption in the processing stage
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 effectively suppresses motion artifacts and noise while preserving edges, achieving real-time performance without the need for complex optical flow calculations or specific camera hardware constraints, enhancing depth map accuracy and usability in dynamic scenes.
Implementation Method 1
Time-of-Flight (ToF) sensor technologies face challenges in handling motion artifacts and noise removal, particularly in dynamic scenes, which affect accurate depth measurements
Implementation Method 2
calculating values of a cross correlation function c(τ) at a plurality of temporally spaced positions or phases from the sent (s(t)) and received (r(t)) signals
Data Source
AI summary
A method and system for real-time motion artifact handling and noise removal for time-of-flight (ToF) sensor images. The method includes: calculating values of a cross correlation function c(τ) at a plurality of temporally spaced positions or phases from sent (s(t)) and received (r(t)) signals, thereby deriving a plurality of respective cross correlation values [c(τ0), c(τ1), c(τ2), c(τ3)]; deriving, from the plurality of cross correlation values [c(τ0), c(τ1), c(τ2), c(τ3)], a depth map D having values representing, for each pixel, distance to a portion of an object upon which the sent signals (s(t)) are incident; deriving, from the plurality of cross correlation values [c(τ0), c(τ1), c(τ2), c(τ3)], a guidance image (I; I′); and generating an output image D′ based on the depth map D and the guidance image (I; I′), the output image D′ comprising an edge-preserving and smoothed version of depth map D, the edge-preserving being from guidance image (I; I′).