Method and apparatus for removing honeycomb artifacts from optical fiber bundle images based on artificial intelligence

A honeycomb artifact removal method for removing honeycomb artifacts in images received from an optical fiber imaging device through a honeycomb artifact removal model, the method comprising:

• creating training data;
• building the honeycomb artifact removal model with the created training data;
• and
• removing a honeycomb artifact in an input image through the built honeycomb artifact removal model and outputting a corrected image,
• wherein creating the training data comprises performing preprocessing of a raw image, acquiring a honeycomb artifact image through the optical fiber imaging device, and synthesizing the preprocessed raw image with the honeycomb artifact image to generate a honeycomb artifact synthesized image, and
• wherein the preprocessing of the raw image includes resizing the raw image so that a size of the raw image matches a size of the honeycomb artifact image, and
• creating the training data is further configured to apply a circular binary mask to the preprocessed raw image or the honeycomb artifact image,
• wherein the training dataset is created by mapping the honeycomb artifact synthesized image as input data and the preprocessed raw image as output data.

A non-transitory computer-readable recording medium storing computer-readable instructions, wherein when the instructions are executed by at least one processor, the instructions enable the at least one processor to perform the steps of:

• creating training data;
• building a honeycomb artifact removal model with the created training data;
• and
• removing a honeycomb artifact in an input image through the built honeycomb artifact removal model and outputting a corrected image,
• wherein creating the training data comprises performing preprocessing of a raw image, acquiring a honeycomb artifact image through the optical fiber imaging device, and synthesizing the preprocessed raw image with the honeycomb artifact image to generate a honeycomb artifact synthesized image, and
• wherein the preprocessing of the raw image includes resizing the raw image so that a size of the raw image matches a size of the honeycomb artifact image, and
• creating the training data is further configured to apply a circular binary mask to the preprocessed raw image or the honeycomb artifact image,
• wherein the training dataset is created by mapping the honeycomb artifact synthesized image as input data and the preprocessed raw image as output data.