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Upsampling

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...

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

Aliasing

Aliasing

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 signal...

Downsampling

Downsampling

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...

Deconvolution

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...

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Related Experiment Video

Updated: Jun 11, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Adaptive multiple-frame image super-resolution based on U-curve.

Qiangqiang Yuan¹, Liangpei Zhang, Huanfeng Shen

¹Wuhan University, China. yqiang86@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 10, 2010

Summary

This summary is machine-generated.

This study introduces an adaptive Maximum a Posteriori (MAP) method for image super-resolution (SR) reconstruction. It uses a U-curve to find the optimal regularization parameter, improving image quality and reducing blur.

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Last Updated: Jun 11, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Image super-resolution (SR) is crucial for recovering high-resolution (HR) images from low-resolution (LR) inputs.
The Maximum a Posteriori (MAP) model is a common framework for SR reconstruction.
Selecting an appropriate regularization parameter in MAP is vital to balance noise suppression and detail preservation.

Purpose of the Study:

To develop an adaptive method for selecting the optimal regularization parameter in MAP-based SR reconstruction.
To enhance the effectiveness and robustness of SR reconstruction algorithms.

Main Methods:

A novel adaptive MAP reconstruction method is proposed.
A U-curve function is constructed using data fidelity and prior terms.
The optimal regularization parameter is identified at the left maximum curvature point of the U-curve.

Main Results:

The proposed method was tested on both simulated and real-world data.
Experimental results demonstrate significant improvements in visual quality.
Quantitative evaluations confirm the method's effectiveness and robustness.

Conclusions:

The U-curve based adaptive MAP method provides an effective approach for SR reconstruction.
This technique successfully optimizes the regularization parameter, leading to superior image recovery.
The algorithm shows promise for practical applications in image enhancement.