scholarly journals Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 362 ◽  
Author(s):  
Chong Fan ◽  
Chaoyun Wu ◽  
Grand Li ◽  
Jun Ma
Keyword(s):  
Remote Sensing ◽  
Point Spread Function ◽  
Convex Sets ◽  
Super Resolution ◽  
Function Estimation ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
Point Spread ◽  
Spread Function

A phase model for point spread function estimation in ground-based astronomy

2013 ◽  
Vol 56 (12) ◽  
pp. 2701-2710 ◽  
Author(s):  
Raymond Honfu Chan ◽  
XiaoMing Yuan ◽  
WenXing Zhang
Keyword(s):  
Point Spread Function ◽  
Phase Model ◽  
Function Estimation ◽  
Point Spread ◽  
Spread Function

scholarly journals High-fidelity structured illumination microscopy by point-spread-function engineering

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Gang Wen ◽  
Simin Li ◽  
Linbo Wang ◽  
Xiaohu Chen ◽  
Zhenglong Sun ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
High Fidelity ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Imaging Tool ◽  
Point Spread ◽  
Spread Function ◽  
Point Spread Function Engineering

AbstractStructured illumination microscopy (SIM) has become a widely used tool for insight into biomedical challenges due to its rapid, long-term, and super-resolution (SR) imaging. However, artifacts that often appear in SIM images have long brought into question its fidelity, and might cause misinterpretation of biological structures. We present HiFi-SIM, a high-fidelity SIM reconstruction algorithm, by engineering the effective point spread function (PSF) into an ideal form. HiFi-SIM can effectively reduce commonly seen artifacts without loss of fine structures and improve the axial sectioning for samples with strong background. In particular, HiFi-SIM is not sensitive to the commonly used PSF and reconstruction parameters; hence, it lowers the requirements for dedicated PSF calibration and complicated parameter adjustment, thus promoting SIM as a daily imaging tool.

scholarly journals Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

2020 ◽  
Author(s):  
Matheus B. Pereira ◽  
Jefersson Alex Dos Santos
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
The Other ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Remote Sensing Images ◽  
Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

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