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Super-resolution Fluorescence Microscopy

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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...

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Updated: Dec 27, 2025

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

Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network.

Xinying Wang^1,2, Yingdan Wu^1,2,3,4, Yang Ming⁵

¹School of Science, Hubei University of Technology, No. 28 Nanli Road, Wuhan 430068, China.

Sensors (Basel, Switzerland)

|February 26, 2020

Summary

This summary is machine-generated.

This study introduces an adaptive multi-scale feature fusion network (AMFFN) for enhancing remote sensing image super-resolution. The novel AMFFN method effectively reconstructs high-resolution details from degraded images, outperforming existing techniques.

Keywords:

adaptive multi-scale feature fusion remote sensing imagery super-resolution

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Last Updated: Dec 27, 2025

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Published on: August 16, 2012

Area of Science:

Computer Vision
Remote Sensing
Image Processing

Background:

Remote sensing image super-resolution is challenging due to complex degradation factors.
Existing methods struggle to infer high-frequency details effectively.

Purpose of the Study:

To propose an adaptive multi-scale feature fusion network (AMFFN) for remote sensing image super-resolution.
To improve the accuracy and quality of super-resolved remote sensing images.

Main Methods:

Feature extraction from low-resolution images.
Utilizing adaptive multi-scale feature extraction (AMFE) modules with squeeze-and-excited and adaptive gating mechanisms for feature fusion.
Reconstructing high-resolution images using sub-pixel convolution.

Main Results:

Experiments on three datasets demonstrate the effectiveness of AMFFN.
Analysis of AMFE module count and gating connection strategies.
Qualitative and quantitative evaluation across different scale factors.

Conclusions:

The proposed AMFFN method achieves superior performance compared to SRCNN, ESPCN, and MSRN.
AMFFN effectively infers high-frequency details in remote sensing imagery.
The adaptive multi-scale approach is crucial for robust super-resolution.