Research on super-resolution reconstruction algorithm of infrared images of compressive coded aperture

2019 ◽  
Author(s):  
Shaojun Chen ◽  
Guihua Fan ◽  
Tinghua Zhang ◽  
Di Liu
Keyword(s):  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Coded Aperture ◽  
Infrared Images

scholarly journals Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4109
Author(s):  
Yan Wang ◽  
Lingjie Wang ◽  
Bingcong Liu ◽  
Hongshan Zhao
Keyword(s):  
Compressed Sensing ◽  
Estimation Method ◽  
Kernel Estimation ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Power Equipment ◽  
Infrared Images ◽  
Blur Kernel Estimation ◽  
Resolution Algorithm ◽  
Blur Kernel

Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory of compressed sensing. It includes an improved blur kernel estimation method combined with compressed sensing theory and an improved infrared image super-resolution reconstruction algorithm based on block compressed sensing theory. In the blur kernel estimation method, we propose a blur kernel estimation algorithm under the compressed sensing framework to realize the estimation of the blur kernel from low-resolution images. In the estimation process, we define a new norm to constrain the gradient image in the iterative process by analyzing the significant edge intensity changes before and after the image is blurred. With the norm, the salient edges can be selected and enhanced, the intermediate latent image generated by the iteration can move closer to the clear image, and the accuracy of the blur kernel estimation can be improved. For the super-resolution reconstruction algorithm, we introduce a blur matrix and a regular total variation term into the traditional compressed sensing model and design a two-step total variation sparse iteration (TwTVSI) algorithm. Therefore, while ensuring the computational efficiency, the boundary effect caused by the block processing inside the image is removed. In addition, the design of the TwTVSI algorithm can effectively process the super-resolution model of compressed sensing with a sparse dictionary, thereby breaking through the reconstruction performance limitation of the traditional regularized super-resolution method of compressed sensing due to the lack of sparseness in the signal transform domain. The final experimental results also verify the effectiveness of our blind super-resolution 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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