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Panasonic Boosts Hyperspectral Sensitivity with Compressed Sensing

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Researchers from Panasonic have developed hyperspectral imaging technology by applying compressed sensing — a method that enables differentiation of slight color differences and enables the improvement of image analysis and recognition accuracy. With the development, the company has achieved what it said is the world’s highest sensitivity in hyperspectral imaging technology.

Conventional hyperspectral imaging uses optical elements such as prisms and filters that selectively pass light of a specific color. Because they detect light separately in each wavelength, there’s a physical restriction in that light utilization efficiency (or sensitivity) decreases in inverse proportion to the number of wavelengths. Therefore, illumination with a brightness comparable to that of the outdoors on a sunny day (illuminance of 10,000 lux or more) was required to shoot, which decreases the usability and versatility.
(a) An image sensor with a special filter. (b) An optical microscope image of the special filter. (c) A schematic image of light detection with thinning out using the special filter. Courtesy of Panasonic.
(a) An image sensor with a special filter. (b) An optical microscope image of the special filter. (c) A schematic image of light detection with thinning out using the special filter. Courtesy of Panasonic.

Compressed sensing can be used to efficiently acquires observation data by “thinning out” and reconstructing data to what it was before using image post-processing. The technique has found use in MRI examinations as well as in observing black holes.

For the method, a filter that transmits multiple wavelengths of light to appropriately thin out data is implemented on an image sensor, and the image reconstruction is carried out by a uniquely optimized algorithm for digital image processing. Panasonic’s filter used a distributed Bragg reflector implemented on an image sensor. The filter is designed to transmit incident light with randomly changing the intensity for each pixel and wavelength. The thinning out of data corresponds to change the intensity for each pixel and wavelength. The data pre-thinning out can be obtained after the data reconstruction process as long as the data is detected with thinning out in an appropriate way.

By leaving part of the color-separating functions to the software, Panasonic’s team overcame the trade-off between the number of wavelengths and sensitivity — the fundamental issue of conventional technology.

The company said the approach made it possible to capture hyperspectral images with the world’s highest sensitivity and video under indoor illumination (550 lux). The researchers achieved a sensitivity of 45% for visible light between 450 and 650 nm (divided into 20 wavelengths in 10-nm increments); a spatial resolution of 3 px for 3-dB contrast; and a frame rate of 32.3 fps. The metrics, the researchers said, are comparable with an equivalent RGB camera and therefore meet the requirements for practical use.
Mar 2023
machine vision
Interpretation of an image of an object or scene through the use of optical noncontact sensing mechanisms for the purpose of obtaining information and/or controlling machines or processes.
The attribute of visual experience that can be described as having quantitatively specifiable dimensions of hue, saturation, and brightness or lightness. The visual experience, not including aspects of extent (e.g., size, shape, texture, etc.) and duration (e.g., movement, flicker, etc.).
Businessimaginghyperspectralmachine visioncompressed sensingImage AnalysiscolorspectroscopyAsia-Pacific

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