Microscopic imaging processing method based on neural network super-resolution technology

Abstract

The invention discloses a microscopic imaging processing method based on a neural network super-resolution technology. The method comprises the following steps: training a full convolutional neural network, arranging the trained full convolutional neural network M on a computer for controlling a microscope, controlling the microscope to shoot a picture, and compensating the shot picture in real time to obtain a clear picture. According to the processing method disclosed by the invention, the shooting speed is greatly improved, the picture quality is improved, and defocusing blurring is inhibited, especially when a plurality of pictures need to be shot for a sample; and even automatic focusing can be replaced, a motor for controlling the lens to move up and down is omitted, and an optical detection system is simplified.

Description

technical field [0001] The invention relates to an image processing method, in particular to a microscopic imaging processing method based on neural network super-resolution technology. Background technique [0002] Microscopic imaging technology is an effective means to observe cells with high temporal and spatial resolution. Before observing the sample, the researcher needs to focus the microscope once, but when the sample area is too large and it needs to be continuously observed, this operation can be problematic: if the sample is curved more than the depth of field of the optical detection system used , some images are clear and some images are blurry. This situation will seriously affect the subsequent analysis and judgment. [0003] Super-resolution technology refers to the process of restoring a high-resolution image from a given low-resolution image by using the relevant knowledge in the fields of digital image processing and computer vision through specific algor...

AI Technical Summary

Problems solved by technology

Before observing the sample, researchers need to focus the microscope once, but when the sample area is too large and it needs to be observed continuously, the above operation will cause problems: if the curvature of the sample is greater than the depth of field of the optical detection system used , some images are clear and some images are blurred

This method is essentially equivalent to sampling in multiple places, which is slow and time-consuming

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