Fluorescent microscopic image restoring method based on blind deconvolution and sparse representation and device thereof

Abstract

The invention provides a fluorescent microscopic image restoring method based on the blind deconvolution and the sparse representation and a device thereof. The method comprises the following steps of: obtaining an observing image of a sample through a fluorescence microscope, and building an objective function based on the sample, wherein the objective function comprises a regular item and a constraint item; obtaining an image restoring iteration formula and a point spreading function iteration formula of the sample according to the objective function; and calculating the observed image through the image restoring iteration formula and the point spreading function iteration formula in order to obtain the restored fluorescent microscopic image and the corrected point spreading function. By using the embodiment of the invention, the contrast ratio and the definition of the restored image are high; the texture details are effectively maintained; the image visual effect is more clear; and naturally, the point spreading function obtained after restoring also has very high degree of accuracy.

Description

technical field [0001] The invention relates to the technical fields of image processing and computer vision, in particular to a method and device for restoring fluorescent microscopic images based on blind deconvolution and sparse representation. Background technique [0002] Fluorescence microscopy is a rapidly developing microscopy technique, which mainly uses fluorescence or phosphorescence to study the properties of samples. Optical section images of biological samples can be obtained using fluorescence microscopy, however, due to the physical limitations of noise and blurring in the imaging process, perfect image information cannot be obtained. Due to photon conversion and the defects of the optical imaging system itself, fluorescence microscopic images cannot avoid the appearance of noise. The statistical distribution of these noises is usually known, so the image noise can usually be removed by appropriate filtering methods. [0003] The blurring process of the imag...

AI Technical Summary

Problems solved by technology

However, the point spread function obtained by the experimental method itself has weak information, is easily disturbed by noise, and can only describe the imaging properties of the microscope in a special case when it is used once. Once the microscope is used again and the conditions change, then the previous time The measured point spread function cannot be applied, nor can it be adjusted and changed according to the actual situation

The point spread function calculated by the theoretical method may change during the imaging process due to the parameters used in the formula, so the calculated point spread function will not match the actual situation

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