JShakyaPsych2012Project

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1.Introduction

Hyperspectral images contain spectral response from each pixel in the scene and hence contain more information than standard RGB image. Based on exact shape of the spectrum of each pixel, it is possible to identify the material composition and distribution in the reflecting surface. If it is a painted surface, it is possible to identify the exact color with same spectral response. In recent years, there has been many advancement in fluroscent microscopy, which uses light spectrum to identify molecules in biological samples. Using hyperspectral camera, we can identify fluorophores and hence spatial distribution of certain molecules. Different illuminants used in cameras have different spectrum and hence using hyperspectral camera, we can identify the illuminant and correct for proper illumination.

The RBG image captured by digital cameras contain 3 colors per pixel. However each pixel in fact has its own charateristic spectrum based on reflectance of that pixel. In RGB cameras, the spectrum is reduced into 3 values (R,G and B) by placing 3 RGB filter in front of 3 CMOS sensors. These filters have their own transmission spectrum so that each filter passes only a limited band of spectrum through it. The 3 number for R,G and B are obtained by multiplying the image spectrum by the corresponding filters. In contrast to RGB camera, a hyperspectral camera, in fact captures the entire spectrum. For doing that the hyperspectral camera receives a line image, passes through a prism which decomposes spectral composition and then various CMOS sensors are different wavelengths capture intensity at each wavelength. In such hyperspectral camera the full 2D view is captured by rotating the camera to acquire successive line images. However this takes time and also requires physical motion in the camera. Often due to scanning nature, it requires scene to be rather static so that it doesn't change while it is scanning. In this project we are proposing a Camera System which can capture hyperspectral images the same way as digital camera in a single shot.

2.Methods

Interferometric Color Filter

Human visual system can only detect intensity and frequency (hence wavelength). However light has various other properties such as polarization, phase etc which our eyes cannot see. However through interference phenomenon we can convert phase difference of two light sources into intensity which we can see.

Illustration of Intereference Phenomenon for variable seperation between mirrors (after Wikipedia)

There has been many recent usage of interference techniques in imaging systems. One of such application is use of interference to create mirrors that selectively reflect a particular wavelength. Such mirrors can be used in designing MEMS based displays. One of such interferometric display technlogies is Mirasol. On other hand the same interference phenomenon can be used to create color filters. When light get transmitted through each partially reflective surfaces (mirrors) seperated by some distances, based on the seperation distance (L) and wavelength of light it is possible to have either constructive or distructive interference. Such a device is called Fabrey-Perot Interferometer. It consists of two semi-transparent mirrors seperated by transparent medium. When light enters the first mirror, part of it is reflected and part of it is transmitted. A portion of transmitted light gets reflected by second mirror, a portion of which gets transmitted back out of first mirror and rest gets re-transmitted in the direction of original incident light. For a particular wavelength and choice of L, if the phase difference between light reflected from first mirror and second mirror is 2*n*pi, they distructively interference hence there is full transmission. The roll-off and width of passband depends on Reflectance of each mirror. The Transmission as function of wavelength and seperation is given by:

$T=\frac{T_1{T}_2}{(1-\sqrt{(R_1{R}_2){)}^2}}\frac{1}{1+4\frac{\sqrt{R_1{R}_2}}{(1-\sqrt{R_1{R}_2}{)}^2}{\sin }^2(\varphi /2)}$ Failed to parse (unknown function "\lamda"): {\displaystyle \phi=\frac{2\pi}{\lamda}2L\cos{(\theta)}}

Where R1,R2,T1 and T3 are reflectances and tranmittances of Mirror 1 and Mirror 2. L is the seperation between mirrors.

MEMS Tunable Interferometric Filters

The interferometric filter describe above can be built as a MEMS device, where two semi-reflective layers on glass substrate are seperated by air. By using electrostatic drive, it is then possible to change the seperation between two mirrors by applying certain voltage. The seperation is a direct function of applied DC voltage, when other factors such as restoring spring force is constant.

• Figure 1 - Filter Transmission Spectra for N number of Filters
• 
  Number of Filters = 2 to 16

Interferometric Hyperspectral Image Sensor

Using above described Tunable color filters, it is possible to take multiple images of a scene applying different filter each time. If the filters' spectral resolution is higher than highest frequency content (in wavelength space) in the scene spectrum, it is possible to represent the scene using images captured with different filters. The number of filters required and their sharpness will depend on nature of the scene. Since most of the natural scene don't have rapid variations in spectrum, it is possible to capture natural hyperspectral scenes using less number of filters than that will be required for spectrum with sharp rapid variations. For example capturing absorption or emission spectra of a many species of molecule will require large number of filters to preserve the shape of the spectrum. In such cases, higher order and hence sharper Interferometric Filters can be used to capture spectral information around sharp peaks and wider filters elsewhere.

3.Results

Organize your results in a good logical order (not necessarily historical order). Include relevant graphs and/or images. Make sure graph axes are labeled. Make sure you draw the reader's attention to the key element of the figure. The key aspect should be the most visible element of the figure or graph. Help the reader by writing a clear figure caption.

4.Conclusions

Describe what you learned. What worked? What didn't? Why? What would you do if you kept working on the project?

5.References

1. US Patent US6400738, “Tunable Fabry-Perot Filters and Laser”
2. Fluorophore Spectra, “http://www.invitrogen.com/site/us/en/home/support/Research-Tools/Fluorescence-SpectraViewer.html”
3. Wikipedia, "http://en.wikipedia.org/wiki/Fabry%E2%80%93P%C3%A9rot_interferometer"
4. Brian A. Wandell, “Foundations of Vision”
5. Ralf Menzel, “Photonics: Linear and Nonlinear Interactions of Laser Light and Matter”
6. Chang Liu, “Foundations of MEMS”
7. B.E.A. Saleh, M.C. Teich, “Fundamentals of Photonics”