Simultaneous spectral recovery and CMOS micro-LED holography with an untrained deep neural network

Abstract

<jats:p>Lensless holography promises compact, low-cost optical apparatus designs with similar performance to traditional imaging setups. Here, we propose the use of a silicon micro-LED fabricated in a commercial CMOS microelectronics process as the illumination source in a lensless holographic microscope. Its small emission area (<jats:inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>&lt;<!-- < --></mml:mo> </mml:mrow> <mml:mn>4</mml:mn> <mml:mspace width="thinmathspace" /> <mml:mtext>µ<!-- µ --></mml:mtext> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:math> </jats:inline-formula>) ensures high spatial coherence without the need for a pinhole and results in a large NA setup, circumventing the limits to the source-to-sample distance encountered by conventional lensless holography apparatus. The scene is reconstructed using an untrained deep neural network architecture that simultaneously performs spectral recovery by learning from the given single experimental diffraction intensity. We envision this synergetic combination of CMOS micro-LEDs and the machine learning framework can be used in other computational imaging applications, such as a compact microscope for live-cell tracking or spectroscopic imaging of biological materials.</jats:p>