Compressive sensing-based super-resolution multispectral imaging system

Bandpass filter–based multispectral (MS) imaging systems have difficulty achieving high-quality MS imaging results while capturing high spatial resolution MS data cubes. This paper proposes a notch filter–based low-cost multicamera MS imaging system that acquires high-resolution MS images. By taking advantage of notch filters to block only specific bands of the spectrum, light from most of the spectrum is allowed to pass through, resulting in a high light efficiency imaging system. A compressive sensing approach is proposed to obtain images of high spatial and spectral resolution. Trained sparse dictionaries are used to perform the spectral and spatial data super-resolution of the acquired images. We simulated the effectiveness of our algorithm on a public dataset and verified the imaging performance of the prototype system by observing natural images. The experimental results show that the spatial resolution can be improved threefold on the laboratory target, the spectral resolution can be improved from 9 to 31 bands, and the average peak signal-to-noise ratio remains at 39. Our prototype imaging system can realize high spatial and spectral resolution imaging results.