A High-Efficiency Super-Resolution Reconstruction Algorithm from Image/Video Sequences

2007 ◽  
Author(s):  
Chuangbai Xiao ◽  
Jing Yu ◽  
Yi Xue
Keyword(s):  
High Efficiency ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Video Sequences

A Robust Space-time Super-resolution Reconstruction Algorithm Based on Multiple Video Sequences

2011 ◽  
Vol 33 (9) ◽  
pp. 2253-2257 ◽  
Author(s):  
Hai-ying Song ◽  
Xiao-hai He ◽  
Yuan-yuan Wu ◽  
Lin-bo Qing
Keyword(s):  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Space Time ◽  
Video Sequences

A super-resolution reconstruction algorithm based on feature fusion

2020 ◽  
Author(s):  
Lin Wang ◽  
Siqi Yang ◽  
Jingqian Jia
Keyword(s):  
Feature Fusion ◽  
Super Resolution ◽  
Reconstruction Algorithm

scholarly journals Double moiré localized plasmon structured illumination microscopy

Nanophotonics ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruslan Röhrich ◽  
A. Femius Koenderink
Keyword(s):  
Near Field ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Experimental Realization ◽  
Spatial Frequencies ◽  
Wide Range ◽  
Localized Plasmon ◽  
Plasmonic Grating

AbstractStructured illumination microscopy (SIM) is a well-established fluorescence imaging technique, which can increase spatial resolution by up to a factor of two. This article reports on a new way to extend the capabilities of structured illumination microscopy, by combining ideas from the fields of illumination engineering and nanophotonics. In this technique, plasmonic arrays of hexagonal symmetry are illuminated by two obliquely incident beams originating from a single laser. The resulting interference between the light grating and plasmonic grating creates a wide range of spatial frequencies above the microscope passband, while still preserving the spatial frequencies of regular SIM. To systematically investigate this technique and to contrast it with regular SIM and localized plasmon SIM, we implement a rigorous simulation procedure, which simulates the near-field illumination of the plasmonic grating and uses it in the subsequent forward imaging model. The inverse problem, of obtaining a super-resolution (SR) image from multiple low-resolution images, is solved using a numerical reconstruction algorithm while the obtained resolution is quantitatively assessed. The results point at the possibility of resolution enhancements beyond regular SIM, which rapidly vanishes with the height above the grating. In an initial experimental realization, the existence of the expected spatial frequencies is shown and the performance of compatible reconstruction approaches is compared. Finally, we discuss the obstacles of experimental implementations that would need to be overcome for artifact-free SR imaging.

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.

Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network

2021 ◽  
Vol 58 (8) ◽  
pp. 0810005
Author(s):  
查体博 Zha Tibo ◽  
罗林 Luo Lin ◽  
杨凯 Yang Kai ◽  
张渝 Zhang Yu ◽  
李金龙 Li Jinlong
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Generative Adversarial Network ◽  
Image Reconstruction Algorithm ◽  
Adversarial Network

Edge area constraint guided filter depth image super-resolution reconstruction algorithm

2021 ◽  
Vol 50 (1) ◽  
pp. 20200081-20200081
Author(s):  
武军安 Jun''an Wu ◽  
郭锐 Rui Guo ◽  
刘荣忠 Rongzhong Liu ◽  
柯尊贵 Zungui Ke ◽  
赵旭 Xu Zhao
Keyword(s):  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Guided Filter ◽  
Depth Image ◽  
Image Super Resolution

Super-Resolution Restoration for Image Based on Entropy Constraint and Projection onto Convex Set

2012 ◽  
Vol 468-471 ◽  
pp. 1041-1048 ◽  
Author(s):  
Xiao Qin Li ◽  
Kang Ling Fang ◽  
Can Jin
Keyword(s):  
Convergence Rate ◽  
Millimeter Wave ◽  
Convex Set ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Spatial Domain ◽  
Imaging Systems ◽  
Resolution Limit ◽  
Classical Algorithm ◽  
Restoration Algorithm

Super-resolution reconstruction for image breaks through the resolution limit of imaging systems without hardware change. The algorithm of projection onto convex set (POCS) is a typical super-resolution reconstruction algorithm in spatial domain. The classical algorithm of POCS lacks the overall constraint for the image, and the convergence rate for iteration is incontrollable. A new super-resolution restoration algorithm for image based on entropy constraint and POCS is proposed in this paper, and experiments with optical and millimeter wave images demonstrate that the new algorithm is effective in improving the precision of super-resolution restoration.

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