A compressed sensing approach for enhancing infrared imaging resolution

This paper presents a novel approach for improving infrared imaging resolution by the use of Compressed Sensing (CS). Instead of sensing raw pixel data, the image sensor measures the compressed samples of the observed image through a coded aperture mask placed on the focal plane of the optical system, and then the image reconstruction can be conducted from these samples using an optimal algorithm. The resolution is determined by the size of the coded aperture mask other than that of the focal plane array (FPA). The attainable quality of the reconstructed image strongly depends on the choice of the coded aperture mode. Based on the framework of CS, we carefully design an optimum mask pattern and use a multiplexing scheme to achieve multiple samples. The gradient projection for sparse reconstruction (GPSR) algorithm is employed to recover the image. The mask radiation effect is discussed by theoretical analyses and numerical simulations. Experimental results are presented to show that the proposed method enhances infrared imaging resolution significantly and ensures imaging quality.

► A compressed sensing method for enhancing infrared imaging resolution is proposed.
► A coded aperture mask is used to coding the incoming beam light before it being recorded.
► A multiplexing scheme makes it possible to obtain multiple samples at one exposure.
► Imaging resolution and quality depends on the coding mode.
► The diffractive effect of the coded aperture mask makes little impact on imaging quality.