Denoising method and device for quantum dot fluorescence image on porous silicon biosensor surface

Abstract

The invention discloses a method and device for denoising a fluorescent image of quantum dots on the surface of a porous silicon biosensor. The method includes: analyzing the noise type in the quantum dot fluorescence image, and determining that the noise type is gamma multiplicative noise; The quantum dot fluorescence image is subjected to non-local mean smoothing, and the filtered image is used as a reference standard image for grayscale compression; multiple homogeneous regions of the noisy fluorescence image are obtained, and the coefficient of variation of each homogeneous region is averaged to obtain the estimated variation. coefficient to determine the number of iterations of grayscale compression; perform grayscale compression preprocessing on the noisy fluorescence image, take the ratio of the smoothed image and the original fluorescence image as the compression coefficient, and compress according to the number of iterations; use the non-local anisotropic diffusion method , calculate the non-local cosine distance of each pixel to obtain the threshold of the diffusion coefficient, solve the differential equation to remove noise and restore the original gray value. The device includes: an analysis module, a smoothing processing module, an acquisition module, a compression module and a restoration module.

Description

technical field [0001] The invention relates to the field of image denoising, in particular to a method and device for denoising a fluorescent image of quantum dots on the surface of a porous silicon biosensor. Background technique [0002] Nanoporous silicon is a new type of nanomaterial. Due to its large specific surface area, good biocompatibility and adjustable refractive index, it can be prepared into high-sensitivity optical sensing devices of various structures, and has been widely used in biological detection. There are two main types of detection mechanisms for PSi optical sensors. The first category is the detection of refractive index changes caused by biological responses. The second category is the detection of fluorescence changes caused by biological reactions. The first type of detection typically employs reflectance spectrometers to measure the shift in reflectance spectrum caused by biological responses. The second type of detection uses a fluorescence s...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem of detection accuracy drop caused by gamma noise in quantum dot / porous silicon biodetection, the present invention provides a method and device for denoising quantum dot fluorescence images on the surface of porous silicon biosensors. The quantum dot fluorescence after processing in the present invention The noise is removed from the image, the original gray value of the image is restored to the greatest extent, and the sensitivity of biological detection based on quantum dot fluorescence image method is greatly improved. See the description below for details:

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