网站论坛建设方案,网站建制作,wordpress多重筛选页面,怎样做自己的手机网站#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 参考文献 4 Matlab代码、数据、文章 1 概述
文献来源 摘要
高速相干拉曼散射成像通过可视化目标分子或细胞内细胞器的时空动力学为揭示细胞机制开辟了一条新途径。通过以MHz调制频率从激光器中提取信号电流激发拉曼散射SRS显微镜已经达到了散粒噪声限制的检测灵敏度。SRS显微镜中基于激光的本振不仅可以产生高水平的信号还可以产生较大的散粒噪声从而降低图像质量和光谱保真度。在这里我们展示了一种去噪算法该算法通过总变异最小化来消除空间和光谱域中的噪声。对于稀释的二甲基亚砜溶液SRS光谱图像的信噪比提高了57倍对于生物组织SRS光谱图像的信噪比提高了15倍。最初埋藏在噪声中的目标分子的弱拉曼峰被解开。将去噪算法与多变量曲线分辨率相结合可以区分秀丽隐杆线虫中富含蛋白质的细胞器的脂肪储存。总之我们的方法在没有帧平均的情况下显着提高了检测灵敏度这对于体内光谱成像非常有用。
关键词
成像处理 无标记显微镜 非线性显微镜 拉曼光谱 原文摘要
High-speed coherent Raman scattering imaging is opening a new avenue to unveiling the cellular machinery by visualizing the spatio-temporal dynamics of target molecules or intracellular organelles. By extracting signals from the laser at MHz modulation frequency, current stimulated Raman scattering (SRS) microscopy has reached shot noise limited detection sensitivity. The laser-based local oscillator in SRS microscopy not only generates high levels of signal, but also delivers a large shot noise which degrades image quality and spectral fidelity. Here, we demonstrate a denoising algorithm that removes the noise in both spatial and spectral domains by total variation minimization. The signal-to-noise ratio of SRS spectroscopic images was improved by up to 57 times for diluted dimethyl sulfoxide solutions and by 15 times for biological tissues. Weak Raman peaks of target molecules originally buried in the noise were unraveled. Coupling the denoising algorithm with multivariate curve resolution allowed discrimination of fat stores from protein-rich organelles in C. elegans . Together, our method significantly improved detection sensitivity without frame averaging, which can be useful for in vivo spectroscopic imaging.
关键词
Imaging processing Label-free microscopy Non-linear microscopy Raman spectroscopy 2 运行结果 主函数代码
clear all close all clc
addpath(genpath(./spectral_tv/));
sel_exp 2; % select an experiment (1 or 2)
% Set the number of rows, columns and frames. M 128; N 128; K 50;
% Load a hyperspectral image (DMSO100%) file_name ../data/DMSO100%.tif; hyper_true read_hyperdata(file_name, M, N, K); [rows, cols, frames] size(hyper_true);
% Add noise in the hyperspectral image if sel_exp1 tmp read_hyperdata(../data/DMSO10%.tif, M, N, K); sigma_true estimate_noise_level(tmp); elseif sel_exp2 sigma_true 0.005:0.005:(0.005*frames); end hyper_noisy zeros(rows, cols, frames); for i1:frames hyper_noisy(:,:,i) hyper_true(:,:,i) sigma_true(i)*randn(rows, cols); end % Spectral Total Variation opts.beta [1 1 0.1]; runtime tic; out_stv spectral_tv(hyper_noisy, opts); runtime_stv toc(runtime); sigma_est out_stv.sigma; psnr_stv psnr(hyper_true, out_stv.f); % Original Total Variation mu 1; opts.w mean(out_stv.w(:)); opts.beta [1 1 0.1]; runtime tic; out_tv deconvtvl2(hyper_noisy, 1, mu, opts); runtime_tv toc(runtime); psnr_tv psnr(hyper_true, out_tv.f); % Print PSNRs between true and denoised images. fprintf(Method: spectral tv, \t psnr: %6.4f, \t runtime: %6.4f\n, psnr_stv, runtime_stv); fprintf(Method: original tv, \t psnr: %6.4f, \t runtime: %6.4f\n, psnr_tv, runtime_tv);
% Plot the true and estimeated noise level. if sel_exp1 || sel_exp2 figure; plot(sigma_true, LineWidth, 2, Color, g); hold on; plot(sigma_est, LineWidth, 2, Color, r); hold off; xlabel(frame); ylabel(noise standard deviation); legend(True, Estimated, Location, best); end
3 参考文献 部分理论来源于网络如有侵权请联系删除。 [1] Chien-Sheng Liao, Joon Hee Choi, Delong Zhang, Stanley H. Chan and Ji-Xin Cheng, Denoising Stimulated Raman Spectroscopic Images by Total Variation Minimization, Journal of Physical Chemistry C, Jul. 2015.
4 Matlab代码、数据、文章
