New super-resolution method reveals fine details without constantly
needing to zoom in

Date:
August 12, 2020
Source:
Texas A&M University
Summary:
Since the early 1930s, electron microscopy has provided
unprecedented access to the alien world of the extraordinarily
small, revealing intricate details that are otherwise impossible
to discern with conventional light microscopy. But to achieve high
resolution over a large specimen area, the energy of the electron
beams needs to be cranked up, which is costly and detrimental to
the specimen under observation.



FULL STORY ========================================================================== Since the early 1930s, electron microscopy has provided unprecedented
access to the alien world of the extraordinarily small, revealing
intricate details that are otherwise impossible to discern with
conventional light microscopy. But to achieve high resolution over a large specimen area, the energy of the electron beams needs to be cranked up,
which is costly and detrimental to the specimen under observation.


========================================================================== Texas A&M University researchers may have found a new method to improve
the quality of low-resolution electron micrographs without compromising
the integrity of specimen samples. By training deep neural networks,
a type of artificial intelligence algorithm, on pairs of images from the
same sample but at different physical resolutions, they have found that
details in lower- resolution images can be enhanced further.

"Normally, a high-energy electron beam is passed through the sample
at locations where greater image resolution is desired. But with our
image processing techniques, we can super resolve an entire image by
using just a few smaller-sized, high-resolution images," said Dr. Yu
Ding, Mike and Sugar Barnes Professor in the Wm Michael Barnes '64
Department of Industrial and Systems Engineering. "This method is less destructive since most parts of the specimen sample needn't be scanned
with high-energy electron beams." The researchers published their image processing technique in Institute of Electric and Electronics Engineers' Transactions on Image Processing in June.

Unlike in light microscopy where photons, or tiny packets of light,
are used to illuminate an object, in electron microscopy, a beam of
electrons is utilized.

The electrons reflected from or passing through the object are then
collected to form an image, called the electron micrograph.

Thus, the energy of the electron beams plays a crucial role in determining
the resolution of images. That is, the higher the energy electrons, the
better the resolution. However, the risk of damaging the specimen also increases, similar to how ultraviolet rays, which are the more energetic relatives of visible light, can damage sensitive materials like the skin.



========================================================================== "There's always that dilemma for scientists," said Ding. "To maintain the specimen's integrity, high-energy electron beams are used sparingly. But
if one does not use energetic beams, high-resolution or the ability
to see at finer scales becomes limited." But there are ways to get
high resolution or super resolution using low- resolution images. One
method involves using multiple low-resolution images of essentially the
same region. Another method learns common patterns between small image
patches and uses unrelated high-resolution images to enhance existing low-resolution images.

These methods almost exclusively use natural light images instead of
electron micrographs. Hence, they run into problems for super-resolving electron micrographs since the underlying physics for light and electron microscopy is different, Ding explained.

The researchers turned to pairs of low- and high-resolution electron microscopic images for a given sample. Although these types of pairs are
not very common in public image databases, they are relatively common
in materials science research and medical imaging.

For their experiments, Ding and his team first took a low-resolution
image of a specimen and then subjected roughly 25% of the area under observation to high- energy electron beams to get a high-resolution
image. The researchers noted that the information in the high-resolution
and low-resolution image pair are very tightly correlated. They said
that this property can be leveraged even though the available dataset
might be small.

For their analyses, Ding and his team used 22 pairs of images of materials infused with nanoparticles. They then divided the high-resolution image
and its equivalent area in the low-resolution image into three by three subimages.

Next, each subimage pair was used to "self-train" deep neural
networks. Post- training, their algorithm became familiar at recognizing
image features, such as edges.

When they tested the trained deep neural network on a new location on the
low- resolution image for which there was no high-resolution counterpart,
they found that their algorithm could enhance features that were hard
to discern by up to 50%.

Although their image processing technique shows a lot of promise, Ding
noted that it still requires a lot of computational power. In the near
future, his team will be directing their efforts in developing algorithms
that are much faster and can be supported by lesser computing hardware.

"Our paired image processing technique reveals details in low-resolution
images that were not discernable before," said Ding. "We are all familiar
with the magic wand feature on our smartphones. It makes the image
clearer. What we aim to do in the long run is to provide the research
community a similar convenient tool for enhancing electron micrographs."

========================================================================== Story Source: Materials provided by Texas_A&M_University. Original
written by Vandana Suresh.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Yanjun Qian, Jiaxi Xu, Lawrence F. Drummy, Yu Ding. Effective Super-
Resolution Methods for Paired Electron Microscopic Images. IEEE
Transactions on Image Processing, 2020; 29: 7317 DOI: 10.1109/
TIP.2020.3000964 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200812201324.htm

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