Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology.

Yang Yang,Wang Wang,Wu Wu,Zhu Zhu,Li Li

doi:10.3390/s19194076

Abstract

Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.

Highlights

The Raman spectrum is a scattering spectrum obtained by the Raman scattering effect
Based on strong molecular specificity [1], Raman spectroscopy has the advantage of non-invasive, high specificity, and high sensitivity [2,3]
Chao et al [13] developed a Raman spectral imaging system for food safety and quality assessment, which was capable of high-spectrum Raman imaging

Summary

Introduction

Based on strong molecular specificity [1], Raman spectroscopy has the advantage of non-invasive, high specificity, and high sensitivity [2,3]. It has a wide range of applications in geology, medicine, archaeology, and chemistry [4,5,6,7,8,9,10,11]. R, G, B three-color channel of the Raman spectral pseudo-color images and the Raman pseudo-color image binarization. Chao et al [13] developed a Raman spectral imaging system for food safety and quality assessment, which was capable of high-spectrum Raman imaging. Qin et al [14] developed a line-scan

Objectives

Methods

Results