Super-Resolution X-Ray Imaging Method and Apparatus

Abstract

In one embodiment, a computing system may obtain a high-resolution X-ray image and a number of low-resolution X-ray images of an object of interest. The system may divide each of the low-resolution X-ray images into a number of low-resolution patches. Each low-resolution patch may be associated with a portion of the object of interest. The system may input a set of low-resolution patches associated with a same portion of the object of interest into a machine-learning model. Each low-resolution patch of the set may be from a different low-resolution X-ray image. The machine-learning model may output a high-resolution patch for the same portion of the object of interest. The system may compare the high-resolution patch outputted by the machine-learning model to a corresponding portion of the high-resolution X-ray image of the object of interest and adjust one or more parameters of the machine-learning model based on the comparison.

What is claimed is: 
     
         1 . A method comprising, by a computing system:
 obtaining a high-resolution X-ray image and a plurality of low-resolution X-ray images of an object of interest;   dividing each low-resolution X-ray image of the plurality of low-resolution X-ray images into a plurality of low-resolution patches, wherein each low-resolution patch is associated with a portion of the object of interest;   inputting a set of low-resolution patches associated with a same portion of the object of interest into a machine-learning model, wherein each low-resolution patch of the set is from a different low-resolution X-ray image, and wherein the machine-learning model outputs a high-resolution patch for the same portion of the object of interest;   comparing the high-resolution patch outputted by the machine-learning model to a corresponding portion of the high-resolution X-ray image of the object of interest; and   adjusting one or more parameters of the machine-learning model based on the comparison.   
     
     
         2 . The method of  claim 1 , further comprising, prior to obtaining the high-resolution X-ray image and the plurality of low-resolution X-ray images:
 determining a target resolution for super-resolution X-ray images outputted by the machine-learning model, wherein the target resolution of the super-resolution X-ray images has a ratio to a resolution of the high-resolution X-ray images, and wherein the resolution of the high-resolution X-ray images has the same ratio to a resolution of the low-resolution X-ray images.   
     
     
         3 . The method of  claim 1 , wherein the machine-learning model is a regression classifier, an artificial neural network, or a support vector machine. 
     
     
         4 . The method of  claim 1 , wherein the high-resolution X-ray image has a resolution equal to an X-ray sensor resolution. 
     
     
         5 . The method of  claim 1 , wherein each low-resolution X-ray image of the plurality of low-resolution X-ray images has a resolution lower than an X-ray sensor resolution. 
     
     
         6 . The method of  claim 1 , wherein the plurality of low-resolution images capture the object of interest from at least two perspectives. 
     
     
         7 . The method of  claim 1 , wherein the plurality of low-resolution images comprises at least two images obtained by:
 changing an imaging condition;   acquiring a low-resolution X-ray image using the imaging condition; and   repeating the changing and acquiring steps until all images of the plurality of low-resolution X-ray images are obtained.   
     
     
         8 . The method of  claim 7 , wherein changing the image condition results in a sub-pixel shift from one low-resolution X-ray image to a next low-resolution X-ray image of the plurality of X-ray images. 
     
     
         9 . The method of  claim 7 , wherein the imaging condition comprises an incident angle of an X-ray beam emitted from an X-ray source of an X-ray system. 
     
     
         10 . The method of  claim 9 , wherein the incident angle is changed by varying a position of the X-ray source. 
     
     
         11 . The method of  claim 7 , wherein the imaging condition comprises a position of an X-ray detector. 
     
     
         12 . The method of  claim 7 , wherein the imaging condition comprises a position of the object of interest. 
     
     
         13 . The method of  claim 1 , further comprising:
 obtaining a set of high-resolution X-ray images of a second object of interest;   dividing each of the high-resolution x-ray images in the set into high-resolution patches;   for each patch region, inputting one or more of the high-resolution patches for the patch region, to an instance of the machine-learning model to generate a super-resolution patch for the patch region; and   generating a super-resolution X-ray image of the second object of interest based on the super-resolution patches for the patch regions.   
     
     
         14 . The method of  claim 13 , wherein the super-resolution patch for the patch region is determined further based on one or more of the high-resolution patches for patch regions that are nearest-neighbors to the patch region. 
     
     
         15 . The method of  claim 13 , further comprising:
 displaying the super-resolution X-ray image on a monitor.   
     
     
         16 . The method of  claim 13 , further comprising:
 detecting one or more defects in the object of interest based on the super-resolution X-ray image.   
     
     
         17 . The method of  claim 13 , further comprising:
 monitoring a manufacturing process of the object of interest based on the super-resolution X-ray image.   
     
     
         18 . The method of  claim 13 , wherein the super-resolution X-ray image has a higher resolution than the high-resolution X-ray image by at least a factor of two. 
     
     
         19 . One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
 obtain a high-resolution X-ray image and a plurality of low-resolution X-ray images of an object of interest;   divide each low-resolution X-ray image of the plurality of low-resolution X-ray images into a plurality of low-resolution patches, wherein each low-resolution patch is associated with a portion of the object of interest;   input a set of low-resolution patches associated with a same portion of the object of interest into a machine-learning model, wherein each low-resolution patch of the set is from a different low-resolution X-ray image, and wherein the machine-learning model outputs a high-resolution patch for the same portion of the object of interest;   compare the high-resolution patch outputted by the machine-learning model to a corresponding portion of the high-resolution X-ray image of the object of interest; and   adjust one or more parameters of the machine-learning model based on the comparison.   
     
     
         20 . A system comprising:
 one or more processors; and   one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to:
 obtain a high-resolution X-ray image and a plurality of low-resolution X-ray images of an object of interest; 
 divide each low-resolution X-ray image of the plurality of low-resolution X-ray images into a plurality of low-resolution patches, wherein each low-resolution patch is associated with a portion of the object of interest; 
 input a set of low-resolution patches associated with a same portion of the object of interest into a machine-learning model, wherein each low-resolution patch of the set is from a different low-resolution X-ray image, and wherein the machine-learning model outputs a high-resolution patch for the same portion of the object of interest; 
 compare the high-resolution patch outputted by the machine-learning model to a corresponding portion of the high-resolution X-ray image of the object of interest; and 
 adjust one or more parameters of the machine-learning model based on the comparison.