Neural Network Generating Spectral Images from Regular Photos
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Solution Overview
Problem
Neural network models trained solely on spectral images require large amounts of labeled data and expensive equipment for spectral image capture, limiting their utility, while models trained on regular images lack the richer data of spectral images, resulting in suboptimal performance.
Innovation Solution
A combined neural network model is trained with a first portion generating spectral images from regular images and a second portion extracting features from spectral images, allowing for improved performance with fewer labeled spectral examples and utilizing regular images for input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a neural network model is trained solely on spectral image training examples, then the model performance is improved, but the requirement for large amounts of labeled spectral image data increases and the cost of spectral image capture equipment increases
Solution Approach 1:
The patent introduces an intermediary approach by training the neural network model using both spectral image training examples and regular image training examples. The spectral image data serves as a mediator to teach the model spectral characteristics, while regular images provide abundant supplementary training data. This combination allows the model to achieve spectral-level performance without requiring exclusively spectral training data, thereby reducing the quantity requirement while maintaining reliability.
Solution Approach 2:
The patent makes the neural network model universal by enabling it to process both spectral image inputs and regular image inputs. The model is trained to recognize that regular images can serve as substitutes for spectral images when spectral data is unavailable. This multi-functionality allows the model to operate effectively across different data types and conditions, reducing dependency on large quantities of specialized spectral training data.
2Reliability
If a neural network model is trained solely on spectral image training examples, then the model performance is improved, but the cost of spectral image capture equipment increases
Solution Approach 1:
The patent applies the copying principle by training the model to create a virtual copy of spectral image data from regular images. Instead of requiring physical spectral imaging devices to capture actual spectral data, the model learns to generate spectral-like representations from conventional images. This virtual copying approach eliminates the need for expensive spectral capture equipment while maintaining model performance.
Solution Approach 2:
The patent substitutes expensive, complex spectral imaging devices with inexpensive regular image cameras. By training the model to process regular images as if they were spectral images, the system replaces costly specialized equipment with affordable, widely available standard cameras, thereby significantly reducing the cost barrier while maintaining effectiveness.
3Ease of manufacture
If a neural network model is trained on regular images, then the cost of equipment decreases, but the model performance deteriorates due to lack of spectral details
Solution Approach 1:
The patent applies parameter changes by transforming the model's processing parameters and feature extraction capabilities. The model is trained to adjust its internal parameters to recognize spectral patterns within regular image data. This parameter transformation allows the model to extract spectral-like information from non-spectral inputs, maintaining performance while using inexpensive equipment.
Solution Approach 2:
The patent introduces another dimension to the model's processing by adding spectral feature extraction capabilities to the standard image processing pipeline. The model learns to operate in an extended feature space that includes spectral dimensions, allowing it to process regular images with spectral-level discrimination ability. This dimensional enhancement resolves the performance limitation without requiring physical spectral imaging equipment.
4Loss of information
If spectral images are used as input, then richer data with higher spectral details is obtained, but the requirement for expensive spectral imaging devices increases
Solution Approach 1:
The patent substitutes the mechanical/optical system of spectral imaging devices with a computational system. Instead of using physical prisms, gratings, or filters to separate spectral components, the model uses neural network computations to extract and represent spectral information from regular images. This substitution replaces expensive hardware with software-based processing, maintaining spectral detail capability while eliminating the need for costly equipment.
Data Source
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AI summary
Systems, methods, and computer readable media related to training and/or using a neural network model. The trained neural network model can be utilized to generate (e.g., over a hidden layer) a spectral image based on a regular image, and to generate output indicative of one or more features present in the generated spectral image (and present in the regular image since the spectral image is generated based on the regular image). As one example, a regular image may be applied as input to the trained neural network model, a spectral image generated over multiple layers of the trained neural network model based on the regular image, and output generated over a plurality of additional layers based on the spectral image. The generated output may be indicative of various features, depending on the training of the additional layers of the trained neural network model.