Topics: Imaging, Inspection, Quantum Computing
Image sensing technology is a highly important capability, used in applications ranging from webcams and smartphone cameras to autonomous vehicles and industrial inspection. The technology offers many other benefits depending on the technology, this can include similar capabilities at a lower cost, increased dynamic range, improved temporal resolution, spatially variable sensitivity, global shutters at high resolution, reducing the unwanted influence of scattering and more. Some of the examples of these technologies are listed as follows. First, hybrid image sensors utilize either organic semiconductors or quantum dots to increase the spectral sensitivity in the SWIR region. Second, extended-range silicon is a much lower cost alternative that can detect light towards the lower end of the SWIR spectral region. Such SWIR sensors could then be employed in vehicles to provide better vision through fog and dust due to reduced scattering. Third, event-based vision is used in autonomous vehicles, drones, and high-speed industrial applications that require image sensing with a high temporal resolution. It is also known as dynamic vision sensing (DVS) as it is an emerging technology that resolves this challenge; it can combine the greater temporal resolution of rapidly changing image regions, with much-reduced data transfer and subsequent processing. Moreover, hyperspectral imaging is another example that is established for precision agriculture and industrial process inspection. However, most hyperspectral cameras currently work on a line-scan principle, while SWIR hyperspectral imaging is restricted to relatively niche applications due to the high cost. Next, the flexible x-ray sensors offer a compelling alternative, since they would be lighter and conformal especially useful for taking images of curved body parts. The fifth example is wavefront imaging. The technology enables the extraction of phase information from incident light that is lost by a conventional sensor; it is used for niche applications such as optical component design and ophthalmology. In summary, increasing the adoption of computational image analysis provides a great opportunity for image sensing technologies since it offers capabilities beyond conventional CMOS sensors.