Neural Multispectral Imaging With Variable Spectral Channels
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Solution Overview
Problem
Conventional image processing devices using neural networks, such as CNNs, often require high computational effort and memory when generating multispectral images with a predefined number of spectral channels, failing to optimize spatial and spectral resolutions for different applications or target scenes, and may output unnecessary amounts of spectral or spatial information.
Innovation Solution
An image processing device and method that utilizes a neural network to generate at least first and second multispectral image data, where the number of spectral channels in the second data is greater than in the first, optimizing the trade-off between spatial and spectral resolutions based on user settings or target applications, using a convolutional neural network (CNN) trained on RGB and multispectral data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network generates multispectral images with a predefined number of spectral channels, then the spectral resolution is improved, but the computational effort and memory requirements increase
Solution Approach 1:
The patent applies dynamics by making the number of spectral channels variable rather than fixed. The neural network dynamically adjusts the number of spectral channels in the output multispectral image data based on the input image characteristics and application requirements. This allows the system to optimize between spectral resolution and computational effort by generating images with different numbers of spectral channels (e.g., first multispectral image data with fewer channels, second with more channels) rather than always using a predefined fixed number.
2Measurement precision
If a neural network generates multispectral images with a predefined number of spectral channels, then the spectral resolution is improved, but the memory requirements increase
Solution Approach 1:
The system dynamically adjusts the number of spectral channels based on actual needs rather than using a fixed predefined number. The neural network can generate first multispectral image data with a first number of spectral channels and second multispectral image data with a second number of spectral channels, allowing memory usage to be optimized according to the specific application requirements and input image characteristics.
3Device complexity
If a neural network uses a fixed number of spectral channels, then the processing is simplified, but the adaptability to different applications and target scenes is reduced
Solution Approach 1:
The patent transforms the fixed processing approach into a dynamic one where the number of spectral channels is adjusted based on application requirements. The neural network receives input image data and generates output multispectral image data with a variable number of spectral channels, enabling adaptation to different applications and target scenes while maintaining manageable processing complexity through automated adjustment.
4Ease of manufacture
If a neural network outputs a fixed amount of spectral information, then the output format is standardized, but the optimization for specific applications is reduced
Solution Approach 1:
The system dynamically adjusts the number of spectral channels in the output based on application-specific requirements rather than using a fixed standardized format. The neural network can generate different numbers of spectral channels (first number for first multispectral image data, second number for second multispectral image data) to optimize the output for specific applications while maintaining a structured output format.
Data Source
AI summary
An image processing device has circuitry, which is configured to obtain input image data being represented by a number of color channels and to input the input image data into a neural network for generating output multispectral image data, wherein the neural network is configured to generate at least first and second multispectral image data on the basis of the input image data, wherein a number of spectral channels of the second multispectral image data is larger than the number of spectral channels of the first multispectral image data.