454 research outputs found
Lensless Imaging by Compressive Sensing
In this paper, we propose a lensless compressive imaging architecture. The
architecture consists of two components, an aperture assembly and a sensor. No
lens is used. The aperture assembly consists of a two dimensional array of
aperture elements. The transmittance of each aperture element is independently
controllable. The sensor is a single detection element. A compressive sensing
matrix is implemented by adjusting the transmittance of the individual aperture
elements according to the values of the sensing matrix. The proposed
architecture is simple and reliable because no lens is used. The architecture
can be used for capturing images of visible and other spectra such as infrared,
or millimeter waves, in surveillance applications for detecting anomalies or
extracting features such as speed of moving objects. Multiple sensors may be
used with a single aperture assembly to capture multi-view images
simultaneously. A prototype was built by using a LCD panel and a photoelectric
sensor for capturing images of visible spectrum.Comment: Accepted ICIP 2013. 5 Pages, 7 Figures. arXiv admin note: substantial
text overlap with arXiv:1302.178
Lensless wide-field fluorescent imaging on a chip using compressive decoding of sparse objects.
We demonstrate the use of a compressive sampling algorithm for on-chip fluorescent imaging of sparse objects over an ultra-large field-of-view (>8 cm(2)) without the need for any lenses or mechanical scanning. In this lensfree imaging technique, fluorescent samples placed on a chip are excited through a prism interface, where the pump light is filtered out by total internal reflection after exciting the entire sample volume. The emitted fluorescent light from the specimen is collected through an on-chip fiber-optic faceplate and is delivered to a wide field-of-view opto-electronic sensor array for lensless recording of fluorescent spots corresponding to the samples. A compressive sampling based optimization algorithm is then used to rapidly reconstruct the sparse distribution of fluorescent sources to achieve approximately 10 microm spatial resolution over the entire active region of the sensor-array, i.e., over an imaging field-of-view of >8 cm(2). Such a wide-field lensless fluorescent imaging platform could especially be significant for high-throughput imaging cytometry, rare cell analysis, as well as for micro-array research
Toward Depth Estimation Using Mask-Based Lensless Cameras
Recently, coded masks have been used to demonstrate a thin form-factor
lensless camera, FlatCam, in which a mask is placed immediately on top of a
bare image sensor. In this paper, we present an imaging model and algorithm to
jointly estimate depth and intensity information in the scene from a single or
multiple FlatCams. We use a light field representation to model the mapping of
3D scene onto the sensor in which light rays from different depths yield
different modulation patterns. We present a greedy depth pursuit algorithm to
search the 3D volume and estimate the depth and intensity of each pixel within
the camera field-of-view. We present simulation results to analyze the
performance of our proposed model and algorithm with different FlatCam
settings
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