Abstract
In the process of biological detection of porous silicon photonic crystals based on quantum dots, the concentration of target organisms can be indirectly measured via the change in the gray value of the fluorescence emitted from the quantum dots in the porous silicon pores before and after the biological reaction on the surface of the device. However, due to the disordered nanostructures in porous silicon and the roughness of the surface, the fluorescence images on the surface contain noise. This paper analyzes the type of noise and its influence on the gray value of fluorescent images. The change in the gray value caused by noise greatly reduces the detection sensitivity. To reduce the influence of noise on the gray value of quantum dot fluorescence images, this paper proposes a denoising method based on gray compression and nonlocal anisotropic diffusion filtering. We used the proposed method to denoise the quantum dot fluorescence image after DNA hybridization in a Bragg structure porous silicon device. The experimental results show that the sensitivity of digital image detection improved significantly after denoising.
Highlights
Porous silicon (PSi) is a new nanomaterial valued for its large specific surface area, good biocompatibility, and adjustable refractive index [1,2], and it has been widely used in the field of biological detection [3,4,5,6]
In Reference [14], the author proposed a phagocytosis method to remove the speckle noise generated by the laser and porous silicon rough surface, which has been proven to improve the accuracy of image-based refractive index detection
We propose a denoising method for quantum dot fluorescence images based on gray value compression and nonlocal anisotropic diffusion to solve this problem
Summary
Porous silicon (PSi) is a new nanomaterial valued for its large specific surface area, good biocompatibility, and adjustable refractive index [1,2], and it has been widely used in the field of biological detection [3,4,5,6]. Reference [15] proposed a digital image gray value detection method based on quantum dot fluorescence labeling. The distribution type of noise in the fluorescence image of quantum dots on the surface of porous silicon is determined by a convolution neural network. To determine the type of noise in the fluorescence emitted by quantum dots in porous silicon, an image noise recognition algorithm based on RESNET was adopted in this paper. To verify the effect of multiplicative gamma noise on the gray level of the quantum dot fluorescence image in porous silicon, multiplicative gamma noise with intensities of where Γ(L) = t The variance is 1/L:. Accurate measurement of the average gray value of quantum dots on the surface of porous silicon devices is key in image detection technology. Gamma noise in fluorescence images reduces the average gray value and the sensitivity of biological detection, so it is very important to eliminate gamma noise in fluorescence images
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