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Related Concept Videos

Upsampling01:22

Upsampling

216
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...
216
Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
140
Bandpass Sampling01:17

Bandpass Sampling

166
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
166
Sampling Theorem01:15

Sampling Theorem

311
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
311
Sampling Methods: Overview01:06

Sampling Methods: Overview

288
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sample tilting for speckle suppression through angular compounding.

Bhaskara Rao Chintada, Pelham Keahey, Néstor Uribe-Patarroyo

    Optics Letters
    |August 29, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Angular compounding using sample tilting suppresses speckle noise in optical coherence tomography (OCT) images. This method enhances image quality without requiring hardware modifications to existing OCT instruments.

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    Area of Science:

    • Biomedical Optics
    • Medical Imaging

    Background:

    • Speckle noise significantly degrades optical coherence tomography (OCT) image quality, hindering accurate interpretation.
    • Existing hardware-based speckle suppression techniques require complex modifications to OCT systems.
    • Algorithmic speckle suppression methods often lack validation due to the absence of physically meaningful ground truth.

    Purpose of the Study:

    • To introduce a novel, hardware-independent method for speckle suppression in OCT images.
    • To demonstrate the efficacy of angular compounding via sample tilting for noise reduction.
    • To validate an approach that utilizes physics-informed image registration.

    Main Methods:

    • Implemented angular compounding by tilting the sample using a motorized rotation stage.
    • Acquired multiple tomograms at different sample tilt angles.
    • Developed a physics-informed affine mapping to relate tomograms acquired at varying tilt angles, retrieved directly from measurement data.

    Main Results:

    • Successfully demonstrated speckle suppression in OCT images through angular compounding.
    • Showcased that sample tilting effectively enhances image quality without altering OCT hardware.
    • Validated the retrieval of a physics-informed affine map directly from acquired data.

    Conclusions:

    • Sample tilting with a motorized stage provides an effective, hardware-agnostic solution for OCT speckle suppression.
    • This method enables angular compounding with existing OCT instruments, improving image interpretability.
    • The physics-informed affine mapping approach offers a robust validation for algorithmic speckle reduction.