Holographic Image Correction Using Depth-Trained Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Holographic image processing systems using self-interference digital holography cameras suffer from reduced image quality due to factors like aberration, shot noise, and imperfect focus, particularly when reproducing images from holograms captured with non-coherent light sources.
Innovation Solution
A holographic image processing apparatus utilizing a neural network to correct holographic images by filtering noise and adjusting color, trained using a training data set generated from holograms captured at different depths, to enhance image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a self-interference digital holography camera is used to capture holograms with non-coherent light sources, then the camera can operate with non-coherent light and has great potential for general-purpose 3D imaging, but the image quality is reduced when the hologram is reproduced
Solution Approach 1:
The patent applies preliminary action by training a neural network in advance using a training data set generated from holograms captured at different depths. The neural network learns to correct aberration, shot noise, and focus issues before the actual holographic imaging is performed. During reproduction, the trained neural network processes the captured hologram to generate a corrected hologram, thereby improving image quality while maintaining the ability to use non-coherent light sources.
2Measurement precision
If neural network correction is applied to the holographic image, then image quality and depth perception are significantly improved, but the processing time and computational complexity increase
Solution Approach 1:
The neural network is trained in advance using a training data set generated from holograms captured at multiple depths. This preliminary training allows the network to learn correction patterns beforehand, so that during actual use, the network can quickly process new holograms without requiring time-consuming training. The trained model applies pre-learned correction algorithms to rapidly improve image quality and depth perception.
Solution Approach 2:
The patent uses a trained neural network model that copies and applies learned correction patterns from the training data set to new holographic images. Instead of performing complex real-time training, the system uses the pre-trained network to replicate successful correction patterns, significantly reducing processing time while maintaining high image quality improvement.
3Measurement precision
If the neural network is trained using holograms from multiple depths, then the correction accuracy and depth perception are enhanced, but the size of the training data set and computational requirements increase
Solution Approach 1:
The patent segments the training process by capturing holograms at multiple discrete depths and organizing them into a structured training data set. Each depth level provides specific correction information for that particular distance, allowing the neural network to learn depth-specific correction patterns. This segmentation enables the system to handle complex depth perception requirements through manageable, organized training data rather than a single monolithic data set.
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 neural network-based correction method significantly improves image quality by accurately focusing and enhancing depth perception in holographic displays, resulting in clearer and more realistic 3D representations.
Implementation Method 1
a processor configured to execute the at least one instruction stored in the memory to generate a corrected holographic image by correcting an original holographic image captured by a holographic camera based on a neural network configured to learn hologram correction in advance
Implementation Method 2
Holography is technology for recording and reproducing three-dimensional information of an object by using diffraction and interference of light
Implementation Method 3
Holography is technology for recording and reproducing three-dimensional information of an object by using diffraction and interference of light
Implementation Method 4
A holographic display is a device for providing images with a sense of depth in a space and may directly reproduce an optical field of an actual three-dimensional image
Data Source
AI summary
Provided is a holographic image processing apparatus including a memory configured to store at least one instruction, and a processor configured to execute the at least one instruction stored in the memory to generate a corrected holographic image by correcting an original holographic image captured by a holographic camera based on a neural network configured to learn hologram correction in advance.