We have developed a compressive hyperspectral imaging system that is based on single-pixel camera architecture. We have incorporated the developed system in a scanning white-light interferometer (SWLI) and showed that replacing SWLI’s CCD-based camera by the compressive hyperspectral imaging system, we have access to high-resolution multispectral images of interferometer’s fringes. Using these multi-spectral images, the system is capable of simultaneous spectroscopy of the surface, which can be used, for example, to eliminate the effect of surface contamination and providing new spectral information for fringe signal analysis which could be used to reduce the need for vertical scan, therefore making height measurement more tolerant to object’s position.
A novel spectral imaging technique is introduced based on a highly dispersive imaging lens system. The chromatic aberration of the lens system is utilized to spread the spectral content of the object over a focal distance. Two three-dimensional surface reconstruction algorithms, depth from focus and depth from defocus, are applied to images captured by dispersive lens system. Using these algorithms, the spectral imager is able to relate either the location of focused image or the amount of defocus at the imaging detector to the spectral content of the object. A spectral imager with ~5 nm spectral resolution is designed based on this technique. The spectral and spatial resolutions of the introduced technique are independent and can be improved simultaneously. Simulation and experimental results are presented.
Pancreatic cancer is the fourth leading cause of cancer death in the United States. Most pancreatic cancer patients will die within the first year of diagnosis, and just 6% will survive five years. Currently, surgery is the only treatment that offers a chance of cure for pancreatic cancer patients. Accurately identifying the tumors margins in real time is a significant difficulty during pancreatic cancer surgery and contributes to the low 5-year survival rate. We are developing a hyperspectral imaging system based on compressive sampling for real-time tumor margin detection to facilitate more effective removal of diseased tissue and result in better patient outcomes. Recent research has shown that optical spectroscopy can be used to distinguish between healthy and diseased tissue and will likely become an important minimally invasive diagnostic tool for a range of diseases. Reflectance spectroscopy provides information about tissue morphology, while laser-induced autofluorescence spectra give accurate information about the content and molecular structure of the emitting tissue. We are developing a spectral imaging system that targets emission from collagen and NAD(P)H as diagnostics for differentiating healthy and diseased pancreatic tissue. In this study, we demonstrate the ability of our camera system to acquire hyperspectral images and its potential application for imaging autofluorescent emission from pancreatic tissue.
Some scenes and objects have a wide range of brightness that cannot be captured with a conventional camera. This limitation, which degrades the dynamic range of an imaged scene or object, is addressed by use of high dynamic range (HDR) imaging techniques. With HDR imaging techniques, images of a very broad range of intensity can be obtained with conventional cameras. Another limitation of conventional cameras is the range of wavelength that they can capture. Outside the visible wavelengths, the responsivity of conventional cameras drops; therefore, a conventional camera cannot capture images in nonvisible wavelengths. Compressive imaging is a solution for this problem. Compressive imaging reduces the number of pixels of a camera to one, so a camera can be replaced by a detector with one pixel. The range of wavelengths to which such detectors are responsive is much wider than that of a conventional camera. A combination of HDR imaging and compressive imaging is introduced and is benefitted from the advantages of both techniques. An algorithm that combines these two techniques is proposed, and results are presented.
For the first time, a high-resolution hyperspectral single-pixel imaging system based on compressive sensing is presented and demonstrated. The system integrates a digital micro-mirror device array to optically compress the image to be acquired and an optical spectrum analyzer to enable high spectral resolution. The system's ability to successfully reconstruct images with 10 pm spectral resolution is proven.
Scenes in real world have dynamic range of radiation that cannot be captured by conventional cameras. High dynamic
range imaging is a technique to capture detail images where, in the field of image, intensity variation is extreme. This
technique is very useful for biological imaging where the samples have very bright and very dark regions and both parts
have useful information. In this article we propose a novel high dynamic range imaging technique based on compressive
imaging that uses one single detector instead of camera (array of detectors) to capture an image. Combination of high
dynamic range imaging and compressive imaging benefits from imaging with high dynamic range of radiation and
advantages of compressive sampling; namely, imaging at regions of optical spectrum where conventional cameras are
not readily available and single detectors are available. Additionally, as its name suggests, this technique requires less
number of samples (compared to raster scanning). Our experimental results show that high dynamic range compressive
imaging system is capable of capturing images with large intensity contrast.
Compressive sensing (CS) has recently emerged and is now a subject of increasing research and discussion, undergoing
significant advances at an incredible pace.
The novel theory of CS provides a fundamentally new approach to data acquisition which overcomes the common
wisdom of information theory, specifically that provided by the Shannon-Nyquist sampling theorem. Perhaps
surprisingly, it predicts that certain signals or images can be accurately, and sometimes even exactly, recovered from
what was previously believed to be highly incomplete measurements (information).
As the requirements of many applications nowadays often exceed the capabilities of traditional imaging architectures,
there has been an increasing deal of interest in so-called computational imaging (CI). CI systems are hybrid imagers in
which computation assumes a central role in the image formation process.
Therefore, in the light of CS theory, we present a transmissive single-pixel camera that integrates a liquid crystal
display (LCD) as an incoherent random coding device, yielding CS-typical compressed observations, since the
beginning of the image acquisition process.
This camera has been incorporated into an optical microscope and the obtained results can be exploited towards the
development of compressive-sensing-based cameras for pixel-level adaptive gain imaging or fluorescence microscopy.
Moiré technique is a technique used for 2D and 3D imaging and surface characterization. Moiré systems may have a
range of zooms to image an object at different levels of details or Moiré images may be combined (or compared) with
images from other interferometers. So, it is needed to inter-relate images together in order to keep the continuity of the
images at different levels of zoom or images from different types of interferometers. This paper uses image registration
techniques to correlate images and find scale and translation between two images. Image registration is widely used in
medical imaging and range imaging to relate two different images from a single object or scene. In this work, only
interferograms from two successive levels of zooms of a Moiré system are used. Saved interferograms are correlated
using one of the affine algorithms which are used in image registration and then relative scale and shift are calculated.
Calculation of these parameters makes it possible to locate the position of area that the Moiré system is zoomed in,
related to the area with lower zoom level. Simulation results show that this technique is applicable and successful in
finding the scale and shift parameters and therefore can keep the continuity between images at different levels of zoom.
A novel combined fiber Bragg grating (FBG) and interferometric based sensor is proposed and demonstrated. The sensor
is based on two overlapped Michelson interferometers working at different wavelengths in a Sagnac loop and two FBGs
used as wavelength selective mirrors. The advantage of the system is that it combines the benefit of point measurement
with FBG and the high sensitivity of long gauge interferometric sensor.