Hyperspectral Signal Processing Device for Compressed Image Extraction
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Solution Overview
Problem
Existing hyperspectral imaging methods require significant computational resources and time to generate hyperspectral images, which can be inefficient for applications requiring rapid processing.
Innovation Solution
A signal processing method that extracts partial image data from compressed hyperspectral image data and generates two-dimensional image data for specific wavelength bands, reducing the amount of calculation and time required for image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compressive sensing is used to obtain hyperspectral images with multiple wavelengths, then resolution and imaging speed are improved, but the amount of calculation and time required to generate the hyperspectral image increases
Solution Approach 1:
The patent divides the hyperspectral image generation process into multiple stages: first generating a compressed image with all wavelength information, then selectively extracting and processing only the necessary wavelength bands in subsequent steps. This segmentation allows the system to maintain high imaging speed while reducing the computational burden of processing all wavelength data simultaneously.
Solution Approach 2:
The patent extracts and processes only the necessary wavelength band information from the compressed hyperspectral image, rather than processing all wavelength data. This extraction approach maintains the resolution and speed benefits of compressive sensing while significantly reducing the calculation time and resources needed for generating the final hyperspectral image.
2Productivity
If compressive sensing is used to obtain hyperspectral images with multiple wavelengths, then resolution and imaging speed are improved, but the computational resources required increase
Solution Approach 1:
The patent segments the computational workload by first creating a compressed representation of all wavelength data, then performing separate processing only for the required wavelength bands. This reduces the total computational resources needed compared to processing the complete hyperspectral dataset at once, while preserving the fast imaging capability.
Solution Approach 2:
The patent extracts only the necessary wavelength band data from the compressed image for further processing, eliminating the need to computationally handle all wavelength information. This extraction strategy maintains imaging speed while significantly reducing the energy and computational resource consumption.
3Loss of time
If partial image data is extracted from compressed hyperspectral image data, then calculation amount and processing time are reduced, but image quality may deteriorate
Solution Approach 1:
The patent performs preliminary compressive sensing to create a compressed image that preserves all wavelength information in a compact form. This preliminary action ensures that when wavelength bands are subsequently extracted and processed, the full spectral information is still available in the compressed representation, preventing quality loss despite processing only partial data.
Solution Approach 2:
The compressed image serves as an intermediary data structure that contains all wavelength information in a space-efficient format. By using this intermediary representation, the system can extract and process individual wavelength bands without losing the underlying spectral information, thereby maintaining image quality while reducing processing time and computational load.
Data Source
AI summary
A signal processing method is performed by a computer. The signal processing method includes: obtaining first compressed image data including hyperspectral information and indicating a two-dimensional image in which the hyperspectral information is compressed, the hyperspectral information being luminance information on each of at least four wavelength bands included in a target wavelength range; extracting partial image data from the first compressed image data; and generating first two-dimensional image data corresponding to a first wavelength band and second two-dimensional image data corresponding to a second wavelength band from the partial image data.