Pursuing the diffraction limit with Nano-LED scanning transmission optical microscopy

Recent research into miniaturized illumination sources has prompted the development of alternative microscopy techniques. Although they are still being explored, emerging nano-light-emitting-diode (nano-LED) technologies show promise in approaching the optical resolution limit in a more feasible manner. This work presents the exploration of their capabilities with two different prototypes. In the first version, a resolution of less than 1 µm was shown thanks to a prototype based on an optically downscaled LED using an LED scanning transmission optical microscopy (STOM) technique. This research demonstrates how this technique can be used to improve STOM images by oversampling the acquisition. The second STOM-based microscope was fabricated with a 200 nm GaN LED. This demonstrates the possibilities for the miniaturization of on-chip-based microscopes

Pursuing the diffraction limit with nano-led scanning transmission optical microscopy

Recent research into miniaturized illumination sources has prompted the development of alternative microscopy techniques. Although they are still being explored, emerging nano-light-emitting-diode (nano-LED) technologies show promise in approaching the optical resolution limit in a more feasible manner. This work presents the exploration of their capabilities with two different prototypes. In the first version, a resolution of less than 1 µm was shown thanks to a prototype based on an optically downscaled LED using an LED scanning transmission optical microscopy (STOM) technique. This research demonstrates how this technique can be used to improve STOM images by oversampling the acquisition. The second STOM-based microscope was fabricated with a 200 nm GaN LED. This demonstrates the possibilities for the miniaturization of on-chip-based microscopes.This work was partially supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 737089—ChipScope

Joint Image and Depth Estimation With Mask-Based Lensless Cameras

Mask-based lensless cameras replace the lens of a conventional camera with a custom mask. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume. Both these approaches fail to recover the scenes with large depth variations. In this paper, we propose a new approach for depth estimation based on an alternating gradient descent algorithm that jointly estimates a continuous depth map and light distribution of the unknown scene from its lensless measurements. We present simulation results on image and depth reconstruction for a variety of 3D test scenes. A comparison between the proposed algorithm and other method shows that our algorithm is more robust for natural scenes with a large range of depths. We built a prototype lensless camera and present experimental results for reconstruction of intensity and depth maps of different real objects

Ultra-fast Lensless Computational Imaging through 5D Frequency Analysis of Time-resolved Light Transport

Light transport has been analyzed extensively, in both the primal domain and the frequency domain. Frequency analyses often provide intuition regarding effects introduced by light propagation and interaction with optical elements; such analyses encourage optimal designs of computational cameras that efficiently capture tailored visual information. However, previous analyses have relied on instantaneous propagation of light, so that the measurement of the time dynamics of light–scene interaction, and any resulting information transfer, is precluded. In this paper, we relax the common assumption that the speed of light is infinite. We analyze free space light propagation in the frequency domain considering spatial, temporal, and angular light variation. Using this analysis, we derive analytic expressions for information transfer between these dimensions and show how this transfer can be exploited for designing a new lensless imaging system. With our frequency analysis, we also derive performance bounds for the proposed computational camera architecture and provide a mathematical framework that will also be useful for future ultra-fast computational imaging systems.MIT Media Lab ConsortiumNatural Sciences and Engineering Research Council of Canad