Compressive Sensing is an innovative technology that significantly reduces the cost of Shortwave Infrared (SWIR) cameras by replacing the camera’s expensive InGaAs Focal Plane Array by low cost components and signal processing algorithms.
InView’s Compressive Sensing technology builds upon $10M of research performed at Rice University, and is protected by 23 patents and patent applications, including US patents: 8,199,244, 7,271,747 and 7,511,643.
Compressive Sensing performs sub-nyquist sampling of images, collecting only enough information at the sampling (sensing) stage as is required to construct an output image. The image being captured passes through a spatial-light modulator which allows the camera to measures the total light energy in half of the image. This measurement step is repeated a number of times, and the series of measurements is used by the camera to reconstruct the image. This measurement process is modeled below.
- X represents the input image (with the two dimensional image information shown shifted into a linear format, and with sparse portions of the image shown as white squares).
- Each row of Φ represents a unique and different spatial-light-modulation pattern.
- Multiplying vector X by one row of Φ gives a product represented by one square of Y.
- A time-series of multiplications using a different row of Φ each time results in the vector Y.
- The reconstruction code then uses Y and Φ to reconstruct the image.