Dynamically Programmable, Dual-Band Computational Imaging System

2012 ◽  
Author(s):  
Brant M. Kaylor ◽  
Amit Ashok ◽  
Eric M. Seger ◽  
Charlie J. Keith ◽  
Randy R. Reibel
Keyword(s):  
Imaging System ◽  
Computational Imaging ◽  
Dual Band

scholarly journals Optical Aberration Calibration and Correction of Photographic System Based on Wavefront Coding

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4011
Author(s):  
Chuanwei Yao ◽  
Yibing Shen
Keyword(s):  
Imaging System ◽  
Optical Design ◽  
Computational Imaging ◽  
Large Field ◽  
Wavefront Aberration ◽  
Imaging Method ◽  
Image Deconvolution ◽  
Pupil Function ◽  
Wavefront Coding ◽  
Blurred Images

The image deconvolution technique can recover potential sharp images from blurred images affected by aberrations. Obtaining the point spread function (PSF) of the imaging system accurately is a prerequisite for robust deconvolution. In this paper, a computational imaging method based on wavefront coding is proposed to reconstruct the wavefront aberration of a photographic system. Firstly, a group of images affected by local aberration is obtained by applying wavefront coding on the optical system’s spectral plane. Then, the PSF is recovered accurately by pupil function synthesis, and finally, the aberration-affected images are recovered by image deconvolution. After aberration correction, the image’s coefficient of variation and mean relative deviation are improved by 60% and 30%, respectively, and the image can reach the limit of resolution of the sensor, as proved by the resolution test board. Meanwhile, the method’s robust anti-noise capability is confirmed through simulation experiments. Through the conversion of the complexity of optical design to a post-processing algorithm, this method offers an economical and efficient strategy for obtaining high-resolution and high-quality images using a simple large-field lens.

scholarly journals Design and Analysis of a W-Band Metasurface-Based Computational Imaging System

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 9911-9918 ◽  
Author(s):  
Tomas Zvolensky ◽  
Jonah N. Gollub ◽  
Daniel L. Marks ◽  
David R. Smith
Keyword(s):  
Imaging System ◽  
Computational Imaging ◽  
W Band

Combined exposure computational imaging system and image restoration method

2021 ◽  
Vol 29 (2) ◽  
pp. 452-462
Author(s):  
Xiao-tian WU ◽  
◽  
Bo LÜ ◽  
Bo LIU ◽  
Hang YANG ◽  
...  
Keyword(s):  
Image Restoration ◽  
Imaging System ◽  
Computational Imaging ◽  
Combined Exposure ◽  
Restoration Method

scholarly journals Frequency-Diverse Holographic Metasurface Antenna for Near-Field Microwave Computational Imaging

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiaqi Han ◽  
Long Li ◽  
Shuncheng Tian ◽  
Xiangjin Ma ◽  
Qiang Feng ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Imaging System ◽  
Near Field ◽  
Horn Antenna ◽  
Computational Imaging ◽  
Simulation Studies ◽  
Field Patterns ◽  
Full Wave ◽  
Imaging Reconstruction ◽  
Iterative Shrinkage

This article presents a holographic metasurface antenna with stochastically distributed surface impedance, which produces randomly frequency-diverse radiation patterns. Low mutual coherence electric field patterns generated by the holographic metasurface antenna can cover the K-band from 18 to 26 GHz with 0.1 GHz intervals. By utilizing the frequency-diverse holographic metasurface (FDHM) antenna, we build a near-field microwave computational imaging system based on reflected signals in the frequency domain. A standard horn antenna is adopted to acquire frequency domain signals radiated from the proposed FDHM antenna. A detail imaging restoration process is presented, and the desired targets are correctly reconstructed using the 81 frequency-diverse patterns through full-wave simulation studies. Compressed sensing technique and iterative shrinkage/thresholding algorithms are applied for the imaging reconstruction. The achieved compressive ratio of this computational imaging system on the physical layer is 30:1.

Compact dual band/dual FOV infrared imaging system with freeform prism

2021 ◽  
Vol 46 (4) ◽  
pp. 829
Author(s):  
Jun Yu ◽  
Zhengxiang Shen ◽  
Zhanshan Wang
Keyword(s):  
Infrared Imaging ◽  
Imaging System ◽  
Dual Band ◽  
Infrared Imaging System

Dual-band submillimeter-wave kinetic inductance bolometers and an imaging system for contraband object detection

2018 ◽  
Author(s):  
Juha Hassel ◽  
Shahab Oddin Dabironezare ◽  
Erio Gandini ◽  
Leif Grönberg ◽  
Hannu Sipola ◽  
...  
Keyword(s):  
Object Detection ◽  
Imaging System ◽  
Submillimeter Wave ◽  
Dual Band ◽  
Kinetic Inductance

scholarly journals Super Field-of-View Lensless Camera by Coded Image Sensors

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1329 ◽  
Author(s):  
Tomoya Nakamura ◽  
Keiichiro Kagawa ◽  
Shiho Torashima ◽  
Masahiro Yamaguchi
Keyword(s):  
Imaging System ◽  
Complementary Metal Oxide Semiconductor ◽  
Image Sensor ◽  
Image Sensors ◽  
Computational Imaging ◽  
Field Of View ◽  
Oxide Semiconductor ◽  
Back Side ◽  
Cmos Image Sensors ◽  
Rich Information

A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor (CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realized super-field-of-view imaging without lenses or coding masks and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

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