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Major Improvement in Lensless Computational Microscopy Achieved

14 August 2017

Source: ITMO NewsSource: ITMO News

Researchers from ITMO University and the Tampere University of Technology have improved computational imaging of optical signals in lensless microscopes. The team employed special algorithms to increase the resolution of obtained images without changing the technical characteristics of the microscopes.

Lensless computational microscopy makes it possible to visualize transparent objects and measure their shape in three dimensions. These microscopes don’t have lenses or objectives that focus light on an image sensor. Instead, lensless microscopes rely on measuring diffraction patterns generated by using a computational approach. There are special algorithms that allow these microscopes to generate the optical image and improve the optical signal itself. Because of this, it is possible to get images with higher resolution only using mathematical methods, without physical changes to the microscopes.

A team of scientists from Russia and Finland turned to computational methods in order to expand the field of view, which is a crucial feature of microscopes. In traditional microscopy, an objective focuses light from a small object area to a bigger object area where the image is captured. This means that the image size appears to be increased. But it is impossible to change the size of the image sensor itself. This is where computational means come in. Computational means allow researchers to overcome the physical limitation of image sensors and expand the field of view.

There are several different diffraction patterns that have to be registered by the camera. In order to perform this task, scientists used special filters called phase mask that are usually synthesized on a computer and fed into the optical path of the microscope with a spatial light modulator. Once the diffraction patterns were processed, the scientists managed to artificially increase the field of view and consequently the resolution of the retrieved image.

"We used the mathematical method of sparse representation of signal. A simple example may help understand how it works. Imagine that you have a grid paper and you choose a square area of 8x8. If you register the signal in this 8x8 square, then the retrieved image will be discretized in the same way. But if the signal meets certain requirements of sparsity, you can potentially use the same 8x8 signal to restore all the missing information regarding the same object, but with a smaller discrete mesh of 16x16 or even 32x32. At the same time, the resolution will increase twofold or fourfold correspondingly. Moreover, our computational algorithm expands the signal beyond the registration area. This essentially implies the appearance of extra pixels around our 8x8 square, which therefore expands the field of view," said Nikolay Petrov, one of the authors of the study and head of the Laboratory of Digital and Display Holography at ITMO University.

The new approach allows scientists to improve image resolution without any modifications in the quality of the image sensor and other microscope components. This suggests significant economy and cheaper microscopes in the future.

Improvement of lensless computational microscopy is a step in the right direction to higher quality research in biology, chemistry, medicine and other fields. A paper on this study was published in The Optical Society.

To contact the author of this article, email Siobhan.Treacy@ieeeglobalspec.com


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