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Related Concept Videos

Upsampling01:22

Upsampling

309
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...
309
Downsampling01:20

Downsampling

251
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...
251
Aliasing01:18

Aliasing

224
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...
224
Bandpass Sampling01:17

Bandpass Sampling

261
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....
261
Sampling Theorem01:15

Sampling Theorem

760
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.
760
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

131
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Spatial-frequency domain aggregation upsampling for pan-sharpening.

Yilong Liu1, Kai Sun2, Yuan Liu1

  • 1School of Mathematics, Northwest University, 229 North Taibai Road, Xi'an, Shaanxi, 710069, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 30, 2025
PubMed
Summary
This summary is machine-generated.

We introduce a new Spatial-Frequency Domain Aggregation Upsampling (SFAU) method to improve remote sensing image quality. SFAU enhances pan-sharpening by better fusing spatial and spectral information, outperforming existing upsampling techniques.

Keywords:
Pan-sharpeningSpatial-frequency domainUpsampling

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Area of Science:

  • Remote Sensing
  • Image Processing
  • Computer Vision

Background:

  • Pan-sharpening is crucial for enhancing remote sensing image quality by fusing panchromatic (PAN) and low-resolution multispectral (LRMS) data.
  • Current deep learning methods for image upsampling in pan-sharpening have limitations in utilizing PAN information and balancing spectral-spatial details.

Purpose of the Study:

  • To propose a novel Spatial-Frequency Domain Aggregation Upsampling (SFAU) method to address limitations in existing pan-sharpening upsampling techniques.
  • To improve the fusion of spatial and spectral information for enhanced remote sensing image quality.

Main Methods:

  • The proposed SFAU method comprises three modules: Dual-Domain Nonlinear Fusion (DDNF), Region-Specific Attention Mechanism (RSAM), and Adaptive Feature Fusion Gate (AFFG).
  • DDNF integrates Frequency-Aware Feature Aggregation (FAFA) and spatial domain enhancement for high-frequency feature capture and detail refinement.
  • RSAM adaptively refines features and preserves spatial-spectral correlations, while AFFG balances the fused information.

Main Results:

  • The SFAU method demonstrated superior performance compared to existing upsampling techniques.
  • Significant performance enhancement was observed for leading pan-sharpening models when integrated with SFAU, especially in challenging high-contrast and spectrally complex regions.
  • The approach exhibited strong generalization capabilities in real-world remote sensing scenarios.

Conclusions:

  • The SFAU method effectively addresses the limitations of current upsampling techniques in pan-sharpening.
  • This novel approach offers a balanced integration of spatial and spectral information, leading to improved remote sensing image quality.
  • SFAU shows significant potential for practical applications in remote sensing image enhancement.