Abstract
Although the potential of spectral imaging has been demonstrated in research environments, its adoption by industry has so far been limited due to the lack of high speed, low cost and compact spectral cameras. We have previously presented work to overcome this limitation by monolithically integrating optical interference filters on top of standard CMOS image sensors for high resolution pushbroom hyperspectral cameras. These cameras require a scanning of the scene and therefore introduce operator complexity due to the need for synchronization and alignment of the scanning to the camera. This typically leads to problems with motion blur, reduced SNR in high speed applications and detection latency and overall restricts the types of applications that can use this system. This paper introduces a novel snapshot multispectral imager concept based on optical filters monolithically integrated on top of a standard CMOS image sensor. By using monolithic integration for the dedicated, high quality spectral filters at its core, it enables the use of mass-produced fore-optics, reducing the total system cost. It overcomes the problems mentioned for scanning applications by snapshot acquisition, where an entire multispectral data cube is sensed at one discrete point in time. This is achieved by applying a novel, tiled filter layout and an optical sub-system which simultaneously duplicates the scene onto each filter tile. Through the use of monolithically integrated optical filters it retains the qualities of compactness, low cost and high acquisition speed, differentiating it from other snapshot spectral cameras based on heterogeneously integrated custom optics. Moreover, thanks to a simple cube assembly process, it enables real-time, low-latency operation. Our prototype camera can acquire multispectral image cubes of 256x256 pixels over 32 bands in the spectral range of 600-1000nm at a speed of about 30 cubes per second at daylight conditions up to 340 cubes per second at higher illumination levels as typically used in machine vision applications.
