A $50,000 Camera you Already Own
Figure 3: With the specially crafted filters, the quantum efficiency of existing sensors can doubled without compromising color accuracy. (Bild: Hc Vision)

Figure 3: With the specially crafted filters, the quantum efficiency of existing sensors can doubled without compromising color accuracy. (Bild: Hc Vision)

Hyperspectral dictionary

The key to the success of the new methodology lies in leveraging a rich hyperspectral prior – embodied by their extensive hyperspectral database, as well as the sparsity of hyperspectral information in natural scenes. This is achieved via a unique, patented, dictionary learning approach: First, several terabytes of hyperspectral data are reduced to a dictionary less than 1MB in size. Then, the dictionary is adapted to the target RGB camera. Finally – the adapted dictionary can be used to recover hyperspectral information from previously unseen images taken with the target RGB camera. This method produces high accuracy results in a general setting, but accuracy can be increased even further by tailoring the hyperspectral prior to a specific application. For example: if one intends to use the system in an agricultural setting, its dictionary may be trained exclusively on agricultural images, thus increasing its accuracy in that setting. The prospect of low cost, snapshot hyperspectral imaging from a handheld device is indeed quite enticing, but HC-Vision’s technology has exciting applications for conventional imaging as well. Since the reconstruction process does not explicitly require a camera with an RGB-like response, optical designs previously unsuited for conventional imaging can now be used without compromising color accuracy. Indeed, over the past year, the company have been developing a camera system with optical filters optimized for both quantum efficiency and hyperspectral reconstruction. On average, this system is twice as light-sensitive as a comparable RGB camera. In addition to improved sensitivity, the system also increases hyperspectral estimation accuracy by over 30 percent. While the testing platform at figure 2 relies on four cameras, the final product will be produced in a single-chip, single-lens configuration using the same manufacturing techniques used for conventional RGB sensors. While these systems are still in active development and not yet available for purchase, they are steadily progressing towards a market-ready version. In addition to their independent imaging platform, HC-Vision are currently negotiating with several industry-leading companies looking to incorporate the new technology into their upcoming products.

More Links

Sparse Recovery of Hyperspectral Signal from Natural RGB Images -https://goo.gl/48UV5Q

ICVL natural hyperspectral image database – http://icvl.cs.bgu.ac.il/hyperspectral

Interdisciplinary Computational Vision Laboratory – http://icvl.bgu.ac.il

Ben-Gurion University of the Negev -www.bgu.ac.il

 

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Hc Vision
www.hc.vision

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Bild: Edmund Optics GmbH
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