Artificial Intelligence Gets Physical | NIH - NIBIB
MBL Fellows Hari Shroff and Daniel Colón-Ramos are among the authors of this new study in Nature Methods.
Neural networks enlist physics-based computations for faster, clearer image restoration
Fluorescence microscopy allows researchers to study specific structures in complex biological samples. However, the image created using fluorescent probes suffers from blurring and background noise. The latest work from NIBIB researchers and their collaborators introduces several novel image restoration strategies that create sharp images with significantly reduced processing time and computing power1.
The cornerstone of modern image processing is the use of artificial intelligence, most notably neural networks that use deep learning to remove the blurring and background noise in an image. The basic strategy is to teach the deep learning network to predict what a blurry, noisy image would look like without the blur and noise. The network must be trained to do this with large datasets of pairs of sharp and fuzzy versions of the same image. A significant barrier to using neural networks is the time and expense needed to create the large training data sets.
Before the use of neural networks, images were cleaned up—known as deconvolution—using equations. Richardson-Lucy Deconvolution (RLD) employs an equation that uses knowledge of the blurring introduced by the microscope to clear up the image. The image is processed through the equation repeatedly to further improve it. Each pass through the equation is known as an iteration and many iterations are needed to create a clear image. The resources and time that it takes to run an image through many iterations is a main drawback of the RLD approach.
RLD is considered to be physics-driven because it describes the physical properties that cause the blurring and noise in an image. Neural networks are said to be data-driven because they must look at a lot of images (data) to learn what constitutes a fuzzy or clear image. The NIBIB team sought to leverage the advantages—and mitigate the disadvantages—of each method by combining them. The result is a neural network that also uses RLD—a Richardson-Lucy Network (RLN). Read rest of the article here.
