Source：Shenzhen Kai Mo Rui Electronic Technology Co. LTD2026-07-09
Traditional camera imaging systems have evolved from monochrome to color photography. A monochrome camera outputs the integral response of its photosensitive sensor to incident light within a specific wavelength band.
As shown in the corresponding graph, the red and black curves represent the Quantum Efficiency (QE) curves of Camera A and Camera B respectively. The final output of a monochrome camera is the integrated light signal over a designated wavelength range, which is determined by the optical filter equipped on the device.
Built upon the imaging principle of monochrome cameras, color cameras adopt Red, Green and Blue (RGB) color filters. This enables the photosensitive sensor to capture light from three independent spectral bands separately, followed by color reconstruction algorithms, thus bringing imaging technology into the color era.
Substances with distinct chemical compositions in nature exhibit different light absorption characteristics at varying wavelengths, which gives rise to diverse colors perceived by human eyes. This serves as the fundamental theoretical basis for color imaging.
Limitations of Conventional Color Imaging
For two different green light sources as illustrated below, a standard color sensor yields identical integrated intensity values and therefore outputs the same signal. In other words, ordinary color imaging lacks the capability to distinguish subtle spectral differences.
Multispectral Imaging
Multispectral imaging allows the sensor to detect light across multiple narrow discrete bands. Basic color imaging essentially only acquires signals from 3 bands (RGB), while multispectral imaging collects image data from 4 to 10 spectral channels, hence the name Multispectral Imaging.
Hyperspectral Imaging
Hyperspectral Imaging adopts even narrower spectral bandwidths and far more sampling bands, generating hundreds of continuous spectral image channels.
Core Application Scenarios
The spectral response curve of vegetation clearly demonstrates extremely high reflectivity in the near-infrared spectrum. This property enables near-infrared imaging to analyze the cellular structure of plants.
Multispectral imaging is widely applied in the food industry. Foreign contaminants such as paper scraps, metal fragments and plastic residues often contaminate food during logistics and processing, posing severe operational risks to food manufacturers. Multispectral inspection effectively identifies such foreign matter pollution.
This technology is also deployed extensively in security surveillance, remote sensing, medical diagnosis and even art authentication.
The major engineering challenge lies in acquiring multiple narrowband spectral images. Two mainstream traditional implementation approaches are listed below:
- Rotating Filter Wheel Method: A set of interchangeable color filters are switched sequentially for frame capture to obtain a series of narrow-spectrum images.
- Multi-Camera Solution: Each individual camera is fitted with a dedicated fixed bandpass filter.
Evidently, both approaches suffer from poor flexibility in practical deployment.
Nowadays, the Fabry–Pérot (FP) Interference Spectral Filter delivers a convenient and highly flexible way to isolate narrowband light. By adjusting the FP cavity length L, the filter can be tuned to target different spectral bands, drastically improving the adaptability and practicability of such systems in engineering projects.
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