Reflectance Sharpening Filter for iToF Depth Accuracy
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Solution Overview
Problem
Existing Time-of-Flight (ToF) camera systems face challenges in accurately determining depth images due to noisy pixels and unwrapping errors, which affect the reliability of depth measurements.
Innovation Solution
An electronic device and method that apply a reflectance sharpening filter to the reflectance image obtained from an indirect Time-of-Flight principle to obtain a filtered reflectance value, allowing for the identification and invalidation of corrupted depth measurements by determining a confidence threshold and comparing it with the filtered reflectance value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a reflectance sharpening filter is applied to the reflectance image, then the accuracy of depth measurements is improved and noisy pixels are removed, but the device complexity increases due to additional processing steps
Solution Approach 1:
The reflectance sharpening filter is applied as a preliminary processing step before depth calculation and validation. By pre-processing the reflectance image to enhance edges and remove noisy pixels, the subsequent depth measurement processes work with cleaner data, improving overall accuracy without requiring complex modifications to the core depth calculation algorithm
Solution Approach 2:
The reflectance sharpening filter acts as an intermediary processing stage between raw reflectance image acquisition and final depth measurement. This intermediate step enhances the quality of input data for depth calculation by removing noisy pixels and sharpening edges, thereby improving measurement precision while keeping the overall system architecture relatively simple
2Reliability
If corrupted depth measurements are identified and invalidated using filtered reflectance values, then the reliability of depth images is improved, but the processing time increases
Solution Approach 1:
The system performs self-validation by using the filtered reflectance values to automatically identify and invalidate corrupted depth measurements. The depth image validation process leverages the already-computed filtered reflectance data to detect inconsistencies and mark corrupted pixels, improving reliability without requiring external validation systems or iterative processing
Solution Approach 2:
The filtered reflectance values provide feedback information that is used to validate the depth measurements. By comparing the filtered reflectance data with the calculated depth values, the system can identify corrupted measurements and invalidate them, creating a feedback loop that improves depth image reliability while using efficiently the already-processed reflectance data
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 removes noisy pixels and improves the accuracy of depth measurements by filtering out corrupted data, enhancing the reliability of depth images captured by ToF cameras.
Implementation Method 1
an illumination unit that illuminates a region of interest with modulated light, and a pixel array that collects light reflected from the same region of interest
Implementation Method 2
A Time-of-Flight (ToF) camera is a range imaging camera system that determines the distance of objects by measuring the time of flight of a light signal between the camera and the object
Implementation Method 3
The depth image can be determined directly from a phase image, which is the collection of all phase delays determined in the pixels of the iToF camera
Data Source
AI summary
An electronic device comprising circuitry configured to apply a reflectance sharpening filter to a reflectance image obtained according to an indirect Time-of-Flight, iToF, principle to obtain a filtered reflectance value for a pixel of the reflectance image.