An official website of the United States government
In this paper, we assess the noise-susceptibility of coherent macroscopic single random phase encoding (SRPE) lensless imaging by analyzing how much information is lost due to the presence of camera noise. We have used numerical simulation to first obtain the noise-free point spread function (PSF) of a diffuser-based SRPE system. Afterwards, we generated a noisy PSF by introducing shot noise, read noise and quantization noise as seen in a real-world camera. Then, we used various statistical measures to look at how the shared information content between the noise-free and noisy PSF is affected as the camera-noise becomes stronger. We have run identical simulations by replacing the diffuser in the lensless SRPE imaging system with lenses for comparison with lens-based imaging. Our results show that SRPE lensless imaging systems are better at retaining information between corresponding noisy and noiseless PSFs under high camera noise than lens-based imaging systems. We have also looked at how physical parameters of diffusers such as feature size and feature height variation affect the noise robustness of an SRPE system. To the best of our knowledge, this is the first report to investigate noise robustness of SRPE systems as a function of diffuser parameters and paves the way for the use of lensless SRPE systems to improve imaging in the presence of image sensor noise.
more »
« less
Hyperuniform scalar random fields for lensless, multispectral imaging systems
We propose a novel framework for the systematic design of lensless imaging systems based on the hyperuniform random field solutions of nonlinear reaction-diffusion equations from pattern formation theory. Specifically, we introduce a new class of imaging point-spread functions (PSFs) with enhanced isotropic behavior and controllable sparsity. We investigate PSFs and modulated transfer functions for a number of nonlinear models and demonstrate that two-phase isotropic random fields with hyperuniform disorder are ideally suited to construct imaging PSFs with improved performances compared to PSFs based on Perlin noise. Additionally, we introduce a phase retrieval algorithm based on non-paraxial Rayleigh–Sommerfeld diffraction theory and introduce diffractive phase plates with PSFs designed from hyperuniform random fields, called hyperuniform phase plates (HPPs). Finally, using high-fidelity object reconstruction, we demonstrate improved image quality using engineered HPPs across the visible range. The proposed framework is suitable for high-performance lensless imaging systems for on-chip microscopy and spectroscopy applications.
more »
« less
- Award ID(s):
- 2015700
- PAR ID:
- 10306749
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Letters
- Volume:
- 46
- Issue:
- 21
- ISSN:
- 0146-9592; OPLEDP
- Format(s):
- Medium: X Size: Article No. 5360
- Size(s):
- Article No. 5360
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We propose an efficient inverse design approach for multifunctional optical elements based on adaptive deep diffractive neural networks (a-D2NNs). Specifically, we introduce a-D2NNs and design two-layer diffractive devices that can selectively focus incident radiation over two well-separated spectral bands at desired distances. We investigate focusing efficiencies at two wavelengths and achieve targeted spectral line shapes and spatial point-spread functions (PSFs) with optimal focusing efficiency. In particular, we demonstrate control of the spectral bandwidths at separate focal positions beyond the theoretical limit of single-lens devices with the same aperture size. Finally, we demonstrate devices that produce super-oscillatory focal spots at desired wavelengths. The proposed method is compatible with current diffractive optics and doublet metasurface technology for ultracompact multispectral imaging and lensless microscopy applications.more » « less
-
We propose a diffuser-based lensless underwater optical signal detection system. The system consists of a lensless one-dimensional (1D) camera array equipped with random phase modulators for signal acquisition and one-dimensional integral imaging convolutional neural network (1DInImCNN) for signal classification. During the acquisition process, the encoded signal transmitted by a light-emitting diode passes through a turbid medium as well as partial occlusion. The 1D diffuser-based lensless camera array is used to capture the transmitted information. The captured pseudorandom patterns are then classified through the 1DInImCNN to output the desired signal. We compared our proposed underwater lensless optical signal detection system with an equivalent lens-based underwater optical signal detection system in terms of detection performance and computational cost. The results show that the former outperforms the latter. Moreover, we use dimensionality reduction on the lensless pattern and study their theoretical computational costs and detection performance. The results show that the detection performance of lensless systems does not suffer appreciably. This makes lensless systems a great candidate for low-cost compressive underwater optical imaging and signal detection.more » « less
-
In this paper, we have used the angular spectrum propagation method and numerical simulations of a single random phase encoding (SRPE) based lensless imaging system, with the goal of quantifying the spatial resolution of the system and assessing its dependence on the physical parameters of the system. Our compact SRPE imaging system consists of a laser diode that illuminates a sample placed on a microscope glass slide, a diffuser that spatially modulates the optical field transmitting through the input object, and an image sensor that captures the intensity of the modulated field. We have considered two-point source apertures as the input object and analyzed the propagated optical field captured by the image sensor. The captured output intensity patterns acquired at each lateral separation between the input point sources were analyzed using a correlation between the captured output pattern for the overlapping point-sources, and the captured output intensity for the separated point sources. The lateral resolution of the system was calculated by finding the lateral separation values of the point sources for which the correlation falls below a threshold value of 35% which is a value chosen in accordance with the Abbe diffraction limit of an equivalent lens-based system. A direct comparison between the SRPE lensless imaging system and an equivalent lens-based imaging system with similar system parameters shows that despite being lensless, the performance of the SRPE system does not suffer as compared to lens-based imaging systems in terms of lateral resolution. We have also investigated how this resolution is affected as the parameters of the lensless imaging system are varied. The results show that SRPE lensless imaging system shows robustness to object to diffuser-to-sensor distance, pixel size of the image sensor, and the number of pixels of the image sensor. To the best of our knowledge, this is the first work to investigate a lensless imaging system’s lateral resolution, robustness to multiple physical parameters of the system, and comparison to lens-based imaging systems.more » « less
-
null (Ed.)Lensless imaging is a new, emerging modality where image sensors utilize optical elements in front of the sensor to perform multiplexed imaging. There have been several recent papers to reconstruct images from lensless imagers, including methods that utilize deep learning for state-of-the-art performance. However, many of these methods require explicit knowledge of the optical element, such as the point spread function, or learn the reconstruction mapping for a single fixed PSF. In this paper, we explore a neural network architecture that performs joint image reconstruction and PSF estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera.more » « less
