Random Sparse Coded Aperture for Lensless Imaging

Abstract

This paper develops a computational lensless imaging system based on a random sparse coded aperture. The camera consists of a thin mask with a coded pattern and a standard sensor array. The proposed coded aperture contains multiple square pinholes, and forms a superposition of multiple pinhole images. In order to reduce the artefact due to diffraction or interference and simultaneously to facilitate the fabrication, the pinholes are designed bigger than some other proposed ones, and sparsely spread on the mask. Only the diffraction pattern for one pinhole imaging model needs be taken into account to improve the angular resolution. An arising issue is that the resulting optical transfer function (OTF) involves many zero-value spectrums, which adversely affects the reconstruction quality with conventional image decoding techniques. We introduce a reselection scheme, which selects partial Fourier samples to reduce the impact of zero entries in OTF. Then, the total variation minimization with quadratic constraints algorithm is applied to attain a good quality reconstruction