Image reconstruction method and system

Abstract

The present invention relates to the image reconstruction technology field, especially to an image reconstruction method and system. The image reconstruction method comprises: the step a: performing frequency spectrum separation of an image, and obtaining the frequency spectrum component of the image; the step b: performing peak point search of the frequency spectrum component, and obtaining the lighting luminous direction and space frequency; the step c: calculating the lighting luminous modulation degree and initial phase through the linear fitting algorithm, performing correction of the frequency spectrum component through lighting luminous modulation degree and initial phase, and performing normalization of the coefficients of the frequency spectrum component; and step d: performing deconvolution and translation processing for the normalization frequency spectrum component, performing frequency spectrum combination of the frequency spectrum component after deconvolution and translation processing, and obtaining the reconstruction result of the image. The image reconstruction method and system set a filter function according to the lighting luminous space frequency initial value, perform filtering of the separation frequency spectrum, and perform peak point search so as to improve the solution precision and effectively improve the quality of the reconstruction image.

Description

technical field [0001] The present application relates to the technical field of image reconstruction, and in particular to an image reconstruction method and system. Background technique [0002] As a kind of optical microscope, fluorescence microscope is an indispensable research tool in modern life science research. However, the resolution of traditional optical microscope is limited by the diffraction limit. In recent years, a series of super-resolution microscopy imaging techniques that break through the diffraction limit have emerged, such as stochastic optical reconstruction microscopy, photoactivation localization microscopy, fluorescence photoactivation localization microscopy, stimulated emission depletion microscopy, structured light illumination microscopy, etc. Structured Illumination Microscope (SIM) is one of the mainstream super-resolution imaging technologies at present. This technology uses structured light loaded with certain frequency information to illu...

AI Technical Summary

Problems solved by technology

The illumination light parameters in the experimental system mainly include parameters such as spatial frequency, direction, modulation degree, and initial phase. Due to the instability of the experimental system, these parameters cannot be known in advance. These parameters can only be solved through a series of original measurement data. Its solution accuracy has an important influence on the quality of the final reconstructed image

[0004] At present, there are mainly the following methods for solving the illumination light parameters: Literature 1 [Phase-shiftsimation in sinusoidally illuminated images for lateral superresolution] determines the position and phase of the peak point of the original data in the frequency domain by analyzing the original data. Spatial frequency and phase, this method is not suitable for high-frequency illumination light and data with large signal noise; Literature 2 [Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy] calculates the components after translation and before Carry out cross-correlation, and then solve the illumination light parameters, but this method has a large amount of calculation, takes a long time, and is not suitable for the rapid processing of SIM data; literature 3[Phase optimization for structuredillumination microscopy] adds iteration on the basis of literature 2 The optimization algorithm speeds up the calculation efficiency of the algorithm, but the algorithm is more complicated, and the calculation accuracy is related to the initial value. Improper initial value will cause the algorithm to fail to converge to the exact value

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