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Register a new userFlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation
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Added by
M. Salman Asif
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light from multiple scene elements. A computational algorithm is then used to demultiplex the recorded measurements and reconstruct an image of the scene. FlatCam is an instance of a coded aperture imaging system; however, unlike the vast majority of related work, we place the coded mask extremely close to the image sensor that can enable a thin system. We employ a separable mask to ensure that both calibration and image reconstruction are scalable in terms of memory requirements and computational complexity. We demonstrate the potential of the FlatCam design using two prototypes: one at visible wavelengths and one at infrared wavelengths.
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Cassegrain designs can be used to build thin lenses. We analyze the relationships between system thickness and aperture sizes of the two mirrors as well as FoV size. Our analysis shows that decrease in lens thickness imposes tight constraint on the aperture and FoV size. To mitigate this limitation, we propose to fill the gaps between the primary and the secondary with high index material. The Gassegrain optics cuts the track length into half and high index material reduces ray angle and height, consequently the incident ray angle can be increased, i.e., the FoV angle is extended. Defining telephoto ratio as the ratio of lens thickness to focal length, we achieve telephoto ratios as small as 0.43 for a visible Cassegrain thin lens and 1.20 for an infrared Cassegrain thin lens. To achieve an arbitrary FoV coverage, we present an strategy by integrating multiple thin lenses on one plane with each unit covering a different FoV region. To avoid physically tilting each unit, we propose beam steering with metasurface. By image stitching, we obtain wide FoV images.
Activity detection from first-person videos (FPV) captured using a wearable camera is an active research field with potential applications in many sectors, including healthcare, law enforcement, and rehabilitation. State-of-the-art methods use optical flow-based hybrid techniques that rely on features derived from the motion of objects from consecutive frames. In this work, we developed a two-stream network, the emph{SegCodeNet}, that uses a network branch containing video-streams with color-coded semantic segmentation masks of relevant objects in addition to the original RGB video-stream. We also include a stream-wise attention gating that prioritizes between the two streams and a frame-wise attention module that prioritizes the video frames that contain relevant features. Experiments are conducted on an FPV dataset containing $18$ activity classes in office environments. In comparison to a single-stream network, the proposed two-stream method achieves an absolute improvement of $14.366%$ and $10.324%$ for averaged F1 score and accuracy, respectively, when average results are compared for three different frame sizes $224times224$, $112times112$, and $64times64$. The proposed method provides significant performance gains for lower-resolution images with absolute improvements of $17%$ and $26%$ in F1 score for input dimensions of $112times112$ and $64times64$, respectively. The best performance is achieved for a frame size of $224times224$ yielding an F1 score and accuracy of $90.176%$ and $90.799%$ which outperforms the state-of-the-art Inflated 3D ConvNet (I3D) cite{carreira2017quo} method by an absolute margin of $4.529%$ and $2.419%$, respectively.
The proposed black-hole finder mission EXIST will consist of multiple wide-field hard X-ray coded-aperture telescopes. The high science goals set for the mission require innovations in telescope design. In particular, wide energy band coverage and fine angular resolution require relatively thick coded masks and thick detectors compared to their pixel size, which may introduce mask self-collimation and depth-induced image blurring with conventional design approaches. Previously we proposed relatively simple solutions to these potential problems: radial hole for mask selfcollimation and cathode depth sensing detector for image blurring. We have now performed laboratory experiments to explore the potential of these two techniques. The experimental results show that the radial hole mask greatly alleviates mask self-collimation and a ~1 mm resolution depth-sensitive detector scheme can be relatively easily achieved for the large scale required for EXIST.
In x-ray coherent scatter tomography, tomographic measurements of the forward scatter distribution are used to infer scatter densities within a volume. A radiopaque 2D pattern placed between the object and the detector array enables the disambiguation between different scatter events. The use of a fan beam source illumination to speed up data acquisition relative to a pencil beam presents computational challenges. To facilitate the use of iterative algorithms based on a penalized Poisson log-likelihood function, efficient computational implementation of the forward and backward models are needed. Our proposed implementation exploits physical symmetries and structural properties of the system and suggests a joint system-algorithm design, where the system design choices are influenced by computational considerations, and in turn lead to reduced reconstruction time. Computational-time speedups of approximately 146 and 32 are achieved in the computation of the forward and backward models, respectively. Results validating the forward model and reconstruction algorithm are presented on simulated analytic and Monte Carlo data.
In this paper we report on the laboratory experiment we settled in the Shanghai Astronomical Observatory (SHAO) to investigate the pyramid wavefront sensor (WFS) ability to measure the differential piston on a sparse aperture. The ultimate goal is to verify the ability of the pyramid WFS work in closed loop to perform the phasing of the primary mirrors of a sparse Fizeau imaging telescope. In the experiment we installed on the optical bench we performed various test checking the ability to flat the wave-front using a deformable mirror and to measure the signal of the differential piston on a two pupils setup. These steps represent the background from which we start to perform full closed loop operation on multiple apertures. These steps were also useful to characterize the achromatic double pyramids (double prisms) manufactured in the SHAO optical workshop.
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