2D multi-layer thickness measurement with reconstructed spectrum
A method for determining thickness of layers of the tear film includes reconstructing a full- or hyper-spectral interference pattern from an imaged multi-spectral pattern. Tear film thickness can then be estimated from the full- or hyper-spectral interference pattern. Using a full- or hyper-spectral interference pattern provides a greater number of frequency sampling points for increased tear film thickness estimation accuracy, without traditional time consuming techniques.
1. A method for measuring layer thickness of a structure comprising:
acquiring a multi-spectral interference pattern of the structure;
performing a hyperspectral reconstruction on the multi-spectral interference pattern, thereby generating a reconstructed full- or hyper-spectral interference pattern; and
estimating the layer thickness based on the reconstructed full- or hyper-spectral interference pattern.
2. The method of claim 1 , wherein the structure is a tear film of an eye.
3. The method of claim 1 , wherein the multi-spectral interference pattern is acquired by capturing a reflected light from the structure with an RGB camera.
4. The method of claim 1 , wherein the multi-spectral interference pattern is acquired by capturing a reflected light from the structure with a dual color camera.
5. The method of claim 1 , wherein the multi-spectral interference pattern is acquired by capturing a reflected light from the structure with a narrow-band multi-spectral camera.
6. The method of claim 1 , wherein the hyperspectral reconstruction on the multi-spectral interference pattern is performed by a machine learning system trained to output the full- or hyper-spectral interference pattern based on an input multi-spectral interference pattern.
7. The method of claim 1 , wherein the layer thickness is estimated by comparing the full- or hyper-spectral interference pattern with a look-up table.
8. The method of claim 1 , wherein the layer thickness is estimated by performing a curve-fitting to the full- or hyper-spectral interference pattern.
9. The method of claim 1 , wherein the layer thickness is estimated by supplying the full- or hyper-spectral interference pattern to a machine learning system trained to output a layer thickness based on the input full- or hyper-spectral interference pattern.
10. The method of claim 1 , further comprising displaying the estimated layer thickness.
11. The method of claim 1 , wherein the acquired multi-spectral interference pattern is out of focus, and the method further comprises focusing the out-of-focus multi-spectral interference pattern prior to performing the hyperspectral reconstruction.
12. The method of claim 11 , wherein the focusing is performed by a machine learning system trained to output an in-focus multi-spectral interference pattern based on an input out-of-focus multi-spectral interference pattern.