Deep CNN-Based Ultrasound Super-Resolution for High-Speed High-Resolution B-Mode Imaging

2018 ◽  
Author(s):  
Woosuk Choi ◽  
Mina Kim ◽  
Jae HakLee ◽  
Jungho Kim ◽  
Jong BeomRa
Keyword(s):  
High Resolution ◽  
High Speed ◽  
Super Resolution ◽  
Deep Cnn

Electrical Probing and Surface Imaging of Deep Sub-Micron Integrated Circuits

1999 ◽  
Author(s):  
Kenneth Krieg ◽  
Richard Qi ◽  
Douglas Thomson ◽  
Greg Bridges
Keyword(s):  
High Resolution ◽  
Integrated Circuits ◽  
Real Time ◽  
High Speed ◽  
Time Signal ◽  
Surface Imaging ◽  
Atomic Force ◽  
Submicron Structures ◽  
High Resolution Images ◽  
Capacitive Loading

Abstract A contact probing system for surface imaging and real-time signal measurement of deep sub-micron integrated circuits is discussed. The probe fits on a standard probe-station and utilizes a conductive atomic force microscope tip to rapidly measure the surface topography and acquire real-time highfrequency signals from features as small as 0.18 micron. The micromachined probe structure minimizes parasitic coupling and the probe achieves a bandwidth greater than 3 GHz, with a capacitive loading of less than 120 fF. High-resolution images of submicron structures and waveforms acquired from high-speed devices are presented.

scholarly journals Cargo sorting zones in the trans-Golgi network visualized by super-resolution confocal live imaging microscopy in plants

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yutaro Shimizu ◽  
Junpei Takagi ◽  
Emi Ito ◽  
Yoko Ito ◽  
Kazuo Ebine ◽  
...  
Keyword(s):  
Membrane Trafficking ◽  
High Speed ◽  
Super Resolution ◽  
Adaptor Protein ◽  
Transport Proteins ◽  
3D Localization ◽  
Golgi Network ◽  
Distinct Distribution ◽  
Vacuolar Trafficking ◽  
Cargo Sorting

AbstractThe trans-Golgi network (TGN) has been known as a key platform to sort and transport proteins to their final destinations in post-Golgi membrane trafficking. However, how the TGN sorts proteins with different destinies still remains elusive. Here, we examined 3D localization and 4D dynamics of TGN-localized proteins of Arabidopsis thaliana that are involved in either secretory or vacuolar trafficking from the TGN, by a multicolor high-speed and high-resolution spinning-disk confocal microscopy approach that we developed. We demonstrate that TGN-localized proteins exhibit spatially and temporally distinct distribution. VAMP721 (R-SNARE), AP (adaptor protein complex)−1, and clathrin which are involved in secretory trafficking compose an exclusive subregion, whereas VAMP727 (R-SNARE) and AP-4 involved in vacuolar trafficking compose another subregion on the same TGN. Based on these findings, we propose that the single TGN has at least two subregions, or “zones”, responsible for distinct cargo sorting: the secretory-trafficking zone and the vacuolar-trafficking zone.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

scholarly journals Ultra-High-Resolution 1 m/pixel CaSSIS DTM Using Super-Resolution Restoration and Shape-from-Shading: Demonstration over Oxia Planum on Mars

2021 ◽  
Vol 13 (11) ◽  
pp. 2185
Author(s):  
Yu Tao ◽  
Sylvain Douté ◽  
Jan-Peter Muller ◽  
Susan J. Conway ◽  
Nicolas Thomas ◽  
...  
Keyword(s):  
High Resolution ◽  
3D Reconstruction ◽  
Imaging System ◽  
Processing System ◽  
Digital Terrain Model ◽  
Super Resolution ◽  
Landing Site ◽  
Shape From Shading ◽  
Stereo Pair ◽  
Terrain Model

We introduce a novel ultra-high-resolution Digital Terrain Model (DTM) processing system using a combination of photogrammetric 3D reconstruction, image co-registration, image super-resolution restoration, shape-from-shading DTM refinement, and 3D co-alignment methods. Technical details of the method are described, and results are demonstrated using a 4 m/pixel Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) panchromatic image and an overlapping 6 m/pixel Mars Reconnaissance Orbiter Context Camera (CTX) stereo pair to produce a 1 m/pixel CaSSIS Super-Resolution Restoration (SRR) DTM for different areas over Oxia Planum on Mars—the future ESA ExoMars 2022 Rosalind Franklin rover’s landing site. Quantitative assessments are made using profile measurements and the counting of resolvable craters, in comparison with the publicly available 1 m/pixel High-Resolution Imaging Experiment (HiRISE) DTM. These assessments demonstrate that the final resultant 1 m/pixel CaSSIS DTM from the proposed processing system has achieved comparable and sometimes more detailed 3D reconstruction compared to the overlapping HiRISE DTM.

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

High-speed high-resolution CMOS voltage comparator

1986 ◽  
Vol 22 (6) ◽  
pp. 338 ◽  
Author(s):  
W.T. Ng ◽  
C.A.T. Salama
Keyword(s):  
High Resolution ◽  
High Speed ◽  
Voltage Comparator
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