Surface Normal-Guided Neural Enhancement for TOF Depth Images
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Solution Overview
Problem
Existing depth image processing methods, particularly using time-of-flight (TOF) cameras, suffer from noise and measurement errors due to camera or subject movement during integration time, leading to inaccurate depth measurements.
Innovation Solution
A method utilizing a cycle-generative adversarial network (GAN) training approach with two neural networks, an enhancement CNN and a noise simulation CNN, to improve depth image quality by removing noise and enhancing sharpness, while maintaining accurate depth information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional TOF depth measurement methods are used, then the measurement process is simple and fast, but noise and measurement errors occur due to camera or subject movement during integration time
Solution Approach 1:
The patent introduces surface normal images as an intermediary element that bridges the input depth image and the corrected depth output. The surface normal information serves as additional contextual data that helps the neural network distinguish between actual depth variations and noise caused by movement, thereby improving measurement precision without requiring fundamental changes to the TOF measurement system itself
Solution Approach 2:
The patent replaces traditional mechanical or algorithmic noise filtering methods with a data-driven neural network approach. Instead of using conventional signal processing techniques to correct depth images, the system employs a trained neural network that learns to distinguish and correct movement-induced noise patterns, achieving higher precision while maintaining system simplicity
2Measurement precision
If noise filtering techniques such as WLS filter are applied, then depth measurement accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network offline using大量 labeled depth image data and corresponding surface normal images. During actual operation, the pre-trained network performs rapid inference without requiring real-time iterative calculations, thus achieving high-quality noise filtering while minimizing processing time and avoiding the computational burden of traditional filters like WLS
Solution Approach 2:
The patent changes the fundamental parameter of processing approach from iterative mathematical optimization (WLS) to direct neural network inference. This parameter change transforms a computationally intensive process into a faster, more efficient operation while maintaining or improving depth image quality through the network's learned noise patterns
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 method effectively enhances depth image quality by reducing noise and improving sharpness, resulting in more accurate depth measurements compared to traditional methods.
Implementation Method 1
A TOF-type camera may project light of a specific wavelength (e.g., a near-infrared ray of 850 nanometers (nm)) to a subject using a light-emitting diode (LED) or a laser diode (LD), measure or photograph light of the same wavelength reflected from the subject with a photodiode or camera, and calculate a phase difference between an irradiated light signal and a signal of the light reflected from the subject
Data Source
AI summary
A method with image processing includes: generating a first surface normal image comprising surface normal vectors corresponding to pixels of a first depth image; and applying the first depth image and the first surface normal image to a first neural network, and acquiring a second depth image by changing the first depth image using the first neural network. The first neural network generates the second depth image to have an improved quality compared to the first depth image, based on an embedding vector that comprises a feature of the first depth image and a feature of the first surface normal image.