scholarly journals Super-Resolution and Large Depth of Field Model for Optical Microscope Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ruo-Peng Zheng ◽  
Shu-Bin Liu ◽  
Lei Li
Keyword(s):  
Network Model ◽  
Signal To Noise Ratio ◽  
Numerical Aperture ◽  
Super Resolution ◽  
Structural Similarity ◽  
Optical Microscope ◽  
Optimal Approximation ◽  
Depth Of Field ◽  
Large Depth ◽  
Potential Applications

Due to the limitation of numerical aperture (NA) in a microscope, it is very difficult to obtain a clear image of the specimen with a large depth of field (DOF). We propose a deep learning network model to simultaneously improve the imaging resolution and DOF of optical microscopes. The proposed M-Deblurgan consists of three parts: (i) a deblurring module equipped with an encoder-decoder network for feature extraction, (ii) an optimal approximation module to reduce the error propagation between the two tasks, and (iii) an SR module to super-resolve the image from the output of the optimal approximation module. The experimental results show that the proposed network model reaches the optimal result. The peak signal-to-noise ratio (PSNR) of the method can reach 37.5326, and the structural similarity (SSIM) can reach 0.9551 in the experimental dataset. The method can also be used in other potential applications, such as microscopes, mobile cameras, and telescopes.

The enhanced deep Plug-and-play super-resolution algorithm with residual channel attention networks

2021 ◽  
pp. 1-10
Author(s):  
Hongguang Pan ◽  
Fan Wen ◽  
Xiangdong Huang ◽  
Xinyu Lei ◽  
Xiaoling Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Super Resolution ◽  
Structural Similarity ◽  
Plug And Play ◽  
Attention Networks ◽  
Frequency Information ◽  
Blind Deblurring ◽  
Adaptive Adjustment ◽  
Deep Feature Extraction

In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively.

WATER-IMMERSION DEEP-SUBWAVELENGTH SURFACE PLASMON VIRTUAL PROBES

2014 ◽  
Vol 02 (02) ◽  
pp. 1440010
Author(s):  
QIAN WANG ◽  
SHIBIAO WEI ◽  
GUANGHUI YUAN ◽  
XIAO-CONG YUAN
Keyword(s):  
Surface Plasmon ◽  
Full Width Half Maximum ◽  
Water Immersion ◽  
Near Field ◽  
Super Resolution ◽  
Optical Microscope ◽  
Optical Manipulation ◽  
Fluorescence Optical Imaging ◽  
Potential Applications ◽  
Near Field Scanning

In this paper, we report the observation of surface plasmon virtual probes in water by using near-field scanning optical microscope. The full-width half-maximum of the probe is as small as λ0/5.5. Such deep-subwavelength sized plasmonic virtual probe may lead to many potential applications, such as super-resolution fluorescence optical imaging and optical manipulation.

Relocating Features Across Microanalytical Instrumentation

1998 ◽  
Vol 4 (S2) ◽  
pp. 524-525
Author(s):  
William H. Powers ◽  
Frederick H. Schamber
Keyword(s):  
Compositional Analysis ◽  
Optical Microscope ◽  
The Other ◽  
Depth Of Field ◽  
Large Depth ◽  
Contrast Mechanisms ◽  
Backscattered Electron ◽  
The Difference ◽  
Distinguishing Features

The ability to investigate a particular feature or region of a specimen using differing kinds of analytic instrumentation can be a very valuable technique. For example, an object viewed under an optical microscope provides color, polarization, and reflectance information which is not available via SEM viewing. Similarly, the SEM with its large depth of field and ability to utilize secondary and backscattered electron contrast mechanisms, and its capability to perform compositional analysis via EDS, provides information which the optical microscope cannot. Thus, the information provided by the two instruments is complementary.Desireable though it may be to view the same feature of a specimen via differing instruments, this is often difficult to execute in practice. The difference in contrast mechanisms which makes the excercise valuable may also make it difficult to relate the distinguishing features viewed under one instrument with those viewed in the other. Further, the issue of scale creates a large complication in many practical instances.

scholarly journals Super-resolution for 2.5D height data of microstructured surfaces using the vdsr network

2020 ◽  
Vol 238 ◽  
pp. 06014
Author(s):  
Stefan Siemens ◽  
Markus Kästner ◽  
Eduard Reithmeier
Keyword(s):  
Laser Scanning ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Super Resolution ◽  
Structural Similarity ◽  
Confocal Laser Scanning Microscope ◽  
Confocal Laser ◽  
Microstructured Surfaces ◽  
Resolution Imaging ◽  
Height Data

In this work super-resolution imaging is used to enhance 2.5D height data of thermal sprayed Al2O3 ceramics with stochastically microstructured surfaces. The data is obtained by means of a confocal laser scanning microscope. By implementing and training a Very Deep Super-Resolution neural network to generate residual images an improvement of the peak signal-to-noise ratio and structural similarity index can be observed when compared to classic interpolation methods.

Relocating Features Across Microanalytical Instrumentation

1998 ◽  
Vol 4 (S2) ◽  
pp. 526-527
Author(s):  
William H. Powers ◽  
Frederick H. Schamber
Keyword(s):  
Compositional Analysis ◽  
Optical Microscope ◽  
The Other ◽  
Depth Of Field ◽  
Large Depth ◽  
Contrast Mechanisms ◽  
Backscattered Electron ◽  
The Difference ◽  
Distinguishing Features

The ability to investigate a particular feature or region of a specimen using differing kinds of analytic instrumentation can be a very valuable technique. For example, an object viewed under an optical microscope provides color, polarization, and reflectance information which is not available via SEM viewing. Similarly, the SEM with its large depth of field and ability to utilize secondary and backscattered electron contrast mechanisms, and its capability to perform compositional analysis via EDS, provides information which the optical microscope cannot. Thus, the information provided by the two instruments is complementary.Desireable though it may be to view the same feature of a specimen via differing instruments, this is often difficult to execute in practice. The difference in contrast mechanisms which makes the excercise valuable may also make it difficult to relate the distinguishing features viewed under one instrument with those viewed in the other. Further, the issue of scale creates a large complication in many practical instances.

scholarly journals iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks

2020 ◽  
Vol 6 (3) ◽  
pp. 307-317
Author(s):  
Aman Chadha ◽  
John Britto ◽  
M. Mani Roja
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Structural Similarity ◽  
Generative Adversarial Networks ◽  
Video Frame ◽  
Perceptual Quality ◽  
Back Projection ◽  
Generative Adversarial Network ◽  
Spatio Temporal

Abstract Recently, learning-based models have enhanced the performance of single-image super-resolution (SISR). However, applying SISR successively to each video frame leads to a lack of temporal coherency. Convolutional neural networks (CNNs) outperform traditional approaches in terms of image quality metrics such as peak signal to noise ratio (PSNR) and structural similarity (SSIM). On the other hand, generative adversarial networks (GANs) offer a competitive advantage by being able to mitigate the issue of a lack of finer texture details, usually seen with CNNs when super-resolving at large upscaling factors. We present iSeeBetter, a novel GAN-based spatio-temporal approach to video super-resolution (VSR) that renders temporally consistent super-resolution videos. iSeeBetter extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its generator. Furthermore, to improve the “naturality” of the super-resolved output while eliminating artifacts seen with traditional algorithms, we utilize the discriminator from super-resolution generative adversarial network. Although mean squared error (MSE) as a primary loss-minimization objective improves PSNR/SSIM, these metrics may not capture fine details in the image resulting in misrepresentation of perceptual quality. To address this, we use a four-fold (MSE, perceptual, adversarial, and total-variation loss function. Our results demonstrate that iSeeBetter offers superior VSR fidelity and surpasses state-of-the-art performance.

scholarly journals End-to-End Super-Resolution for Remote-Sensing Images Using an Improved Multi-Scale Residual Network

2021 ◽  
Vol 13 (4) ◽  
pp. 666
Author(s):  
Hai Huan ◽  
Pengcheng Li ◽  
Nan Zou ◽  
Chao Wang ◽  
Yaqin Xie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Signal To Noise Ratio ◽  
Model Performance ◽  
Super Resolution ◽  
Structural Similarity ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Multi Scale ◽  
Information Image

Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.

scholarly journals Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Li Li ◽  
Zijia Fan ◽  
Mingyang Zhao ◽  
Xinlei Wang ◽  
Zhongyang Wang ◽  
...  
Keyword(s):  
Network Architecture ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Structural Similarity ◽  
Image Sequence ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Underwater Image ◽  
Dual Generator ◽  
The Stability

Since the underwater image is not clear and difficult to recognize, it is necessary to obtain a clear image with the super-resolution (SR) method to further study underwater images. The obtained images with conventional underwater image super-resolution methods lack detailed information, which results in errors in subsequent recognition and other processes. Therefore, we propose an image sequence generative adversarial network (ISGAN) method for super-resolution based on underwater image sequences collected by multifocus from the same angle, which can obtain more details and improve the resolution of the image. At the same time, a dual generator method is used in order to optimize the network architecture and improve the stability of the generator. The preprocessed images are, respectively, passed through the dual generator, one of which is used as the main generator to generate the SR image of sequence images, and the other is used as the auxiliary generator to prevent the training from crashing or generating redundant details. Experimental results show that the proposed method can be improved on both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to the traditional GAN method in underwater image SR.

Cryomicroscopy of crotoxin complex crystals at 400 KV

1989 ◽  
Vol 47 ◽  
pp. 826-827
Author(s):  
Jaap Brink ◽  
Wah Chiu
Keyword(s):  
Signal To Noise Ratio ◽  
3D Structure ◽  
Three Dimensional ◽  
Carbon Films ◽  
Depth Of Field ◽  
Basic Subunit ◽  
Ewald Sphere ◽  
Diffraction Patterns ◽  
Complex Crystals ◽  
Acidic Subunit

The crotoxin complex is a potent neurotoxin composed of a basic subunit (Mr = 12,000) and an acidic subunit (M = 10,000). The basic subunit possesses phospholipase activity whereas the acidic subunit shows no enzymatic activity at all. The complex's toxocity is expressed both pre- and post-synaptically. The crotoxin complex forms thin crystals suitable for electron crystallography. The crystals diffract up to 0.16 nm in the microscope, whereas images show reflections out to 0.39 nm2. Ultimate goal in this study is to obtain a three-dimensional (3D-) structure map of the protein around 0.3 nm resolution. Use of 100 keV electrons in this is limited; the unit cell's height c of 25.6 nm causes problems associated with multiple scattering, radiation damage, limited depth of field and a more pronounced Ewald sphere curvature. In general, they lead to projections of the unit cell, which at the desired resolution, cannot be interpreted following the weak-phase approximation. Circumventing this problem is possible through the use of 400 keV electrons. Although the overall contrast is lowered due to a smaller scattering cross-section, the signal-to-noise ratio of especially higher order reflections will improve due to a smaller contribution of inelastic scattering. We report here our preliminary results demonstrating the feasability of the data collection procedure at 400 kV.Crystals of crotoxin complex were prepared on carbon-covered holey-carbon films, quench frozen in liquid ethane, inserted into a Gatan 626 holder, transferred into a JEOL 4000EX electron microscope equipped with a pair of anticontaminators operating at −184°C and examined under low-dose conditions. Selected area electron diffraction patterns (EDP's) and images of the crystals were recorded at 400 kV and −167°C with dose levels of 5 and 9.5 electrons/Å, respectively.

On resolving fine periodic microstructures in the optical microscope

1989 ◽  
Vol 47 ◽  
pp. 364-365
Author(s):  
W.S. Putnam ◽  
C. Viney
Keyword(s):  
Liquid Crystalline ◽  
Numerical Aperture ◽  
Polarized Light ◽  
Normal Incidence ◽  
Optical Microscope ◽  
Angle Of Incidence ◽  
Resolution Limit ◽  
Crystalline Materials ◽  
Periodic Microstructures ◽  
Liquid Crystalline Materials

Many sheared liquid crystalline materials (fibers, films and moldings) exhibit a fine banded microstructure when observed in the polarized light microscope. In some cases, for example Kevlar® fiber, the periodicity is close to the resolution limit of even the highest numerical aperture objectives. The periodic microstructure reflects a non-uniform alignment of the constituent molecules, and consequently is an indication that the mechanical properties will be less than optimal. Thus it is necessary to obtain quality micrographs for characterization, which in turn requires that fine detail should contribute significantly to image formation.It is textbook knowledge that the resolution achievable with a given microscope objective (numerical aperture NA) and a given wavelength of light (λ) increases as the angle of incidence of light at the specimen surface is increased. Stated in terms of the Abbe resolution criterion, resolution improves from λ/NA to λ/2NA with increasing departure from normal incidence.

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