相关实验视频

Updated: May 28, 2025

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

通过局部低级张量核规范和深度张量先验的福利埃单像素光谱成像

Zixin Tang, Yexun Hu, Chen Duo

    Optics letters
    |February 14, 2025
    PubMed
    概括

    这项研究引入了一种新的福里埃单像素光谱成像 (FSSI) 技术,以提高低采样比率 (SR) 的图像质量. 该方法增强了光谱图像重建,显著提高了成像性能.

    更多相关视频

    Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy10:03

    Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy

    Published on: June 27, 2014

    17.9K
    A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors11:15

    A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

    Published on: May 30, 2016

    25.1K

    相关概念视频

    Fast Fourier Transform01:10

    Fast Fourier Transform

    The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
    The computational efficiency of the FFT becomes...
    258
    Two-Dimensional (2D) NMR: Overview01:12

    Two-Dimensional (2D) NMR: Overview

    The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
    The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
    607
    Discrete-Time Fourier Series01:20

    Discrete-Time Fourier Series

    The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
    For a discrete-time periodic signal x[n]...
    211
    Upsampling01:22

    Upsampling

    Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
    193
    Continuous -time Fourier Transform01:11

    Continuous -time Fourier Transform

    The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
    275
    Deconvolution01:20

    Deconvolution

    Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
    Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
    129