Super-resolution X-ray imaging method and apparatus
The presently-disclosed technology improves the resolution of an x-ray microscope so as to obtain super-resolution x-ray images having resolutions beyond the maximum normal resolution of the x-ray microscope. Furthermore, the disclosed technology provides for the rapid generation of the super-resolution x-ray images and so enables real-time super-resolution x-ray imaging for purposes of defect detection, for example. A method of super-resolution x-ray imaging using a super-resolving patch classifier is provided. In addition, a method of training the super-resolving patch classifier is disclosed. Other embodiments, aspects and features are also disclosed.
1. A method of super-resolution x-ray imaging, the method comprising:
obtaining a set of high-resolution x-ray images of an object;
dividing each of the high-resolution x-ray images in the set into high-resolution patches;
for each patch region, inputting the high-resolution patches corresponding to the patch region to an instance of a trained super-resolving patch classifier to generate a super-resolution patch for the patch region; and
stitching together the super-resolution patches for the patch regions to obtain a super-resolution x-ray image of the object.
2. The method of claim 1 , wherein the set of high-resolution x-ray images of the object are obtained by
changing an imaging condition;
acquiring a high-resolution x-ray image using the imaging condition; and
repeating the changing and acquiring steps until the set of high-resolution x-ray images is complete.
3. The method of claim 2 , wherein changing the imaging condition results in a sub-pixel shift from one high-resolution x-ray image to a next high-resolution x-ray image in the set.
4. The method of claim 3 , wherein the imaging condition comprises an incident angle of the x-ray beam.
5. The method of claim 4 , wherein the incident angle is changed by varying a position of an x-ray source.
6. The method of claim 3 , wherein the imaging condition comprises a position of an x-ray detector.
7. The method of claim 3 , wherein the imaging condition comprises a position of the object.
8. The method of claim 1 , wherein the super-resolving patch classifier comprises a regression classifier.
9. The method of claim 8 , wherein the regression classifier comprises an artificial neural network.
10. The method of claim 8 , wherein the regression classifier comprises a support vector machine.
11. The method of claim 1 , further comprising:
displaying the super-resolution x-ray image on a monitor.
12. The method of claim 1 , further comprising:
using the super-resolution x-ray image for detecting defects in the object.
13. The method of claim 1 , wherein the super-resolution x-ray image is used for process monitoring.
14. The method of claim 1 , wherein the super-resolution x-ray image has a resolution that is smaller in size than the high-resolution x-ray image by at least a factor of two.
15. An apparatus for super-resolution x-ray imaging of an object, the apparatus comprising:
an x-ray imager; and
a computer system for controlling the x-ray imager, wherein the computer system comprises a super-resolving imaging module, the super-resolution imaging module performing steps including:
obtaining a set of high-resolution x-ray images of the object;
dividing each of the high-resolution x-ray images in the set into high-resolution patches;
for each patch region, inputting the high-resolution patches corresponding to the patch region to an instance of a trained super-resolving patch classifier to generate a super-resolution patch for the patch region; and
stitching together the super-resolution patches for the patch regions to obtain a super-resolution x-ray image of the object.
16. The apparatus of claim 15 , wherein the set of high-resolution x-ray images of the object are obtained by the super-resolution imaging module performing steps of:
changing an imaging condition;
acquiring a high-resolution x-ray image using the imaging condition; and
repeating the changing and acquiring steps until the set of high-resolution x-ray images is complete.
17. The apparatus of claim 16 , wherein the imaging condition comprises an incident angle of the x-ray beam.
18. The apparatus of claim 17 , wherein the super-resolution imaging module changes the incident angle by varying a position of an x-ray source.
19. The apparatus of claim 16 , wherein the imaging condition comprises a position of an x-ray detector.
20. The apparatus of claim 16 , wherein the imaging condition comprises a position of the object.
21. The apparatus of claim 15 , wherein the super-resolving patch classifier comprises a regression classifier.
22. The apparatus of claim 21 , wherein the regression classifier comprises an artificial neural network.
23. The apparatus of claim 21 , wherein the regression classifier comprises a support vector machine.
24. The apparatus of claim 15 , wherein the apparatus displays the super-resolution image on a monitor.
25. The apparatus of claim 15 , wherein the apparatus comprises an inspection machine that uses the super-resolution x-ray image for detecting defects in the object.
26. The apparatus of claim 15 , wherein the super-resolution x-ray image is used for process monitoring.
27. The apparatus of claim 15 , wherein the super-resolution x-ray image has a resolution that is smaller in size than the high-resolution x-ray image by at least a factor of two.