Optical image hiding under low light illumination based on deep learning

2021 ◽  
pp. 127842
Author(s):  
Guo Yang ◽  
Shujie Zou ◽  
Jiaosheng Li ◽  
Yuhui Li ◽  
Jun Li
Keyword(s):  
Deep Learning ◽  
Optical Image ◽  
Light Illumination ◽  
Low Light ◽  
Image Hiding

Low-light-level three-dimensional video microscope with long working distance objective lenses

1994 ◽  
Vol 52 ◽  
pp. 226-227
Author(s):  
W. Lin ◽  
J. Gregorio ◽  
T.J. Holmes ◽  
D. H. Szarowski ◽  
J.N. Turner
Keyword(s):  
Three Dimensional ◽  
Light Level ◽  
Stereo Pair ◽  
Light Illumination ◽  
Initial Image ◽  
Low Light ◽  
Image Recording ◽  
Depth Discrimination ◽  
Video Microscope ◽  
Working Distance

A low-light level video microscope with long working distance objective lenses has been built as part of our integrated three-dimensional (3-D) light microscopy workstation (Fig. 1). It allows the observation of living specimens under sufficiently low light illumination that no significant photobleaching or alternation of specimen physiology is produced. The improved image quality, depth discrimination and 3-D reconstruction provides a versatile intermediate resolution system that replaces the commonly used dissection microscope for initial image recording and positioning of microelectrodes for neurobiology. A 3-D image is displayed on-line to guide the execution of complex experiments. An image composed of 40 optical sections requires 7 minutes to process and display a stereo pair.The low-light level video microscope utilizes long working distance objective lenses from Mitutoyo (10X, 0.28NA, 37 mm working distance; 20X, 0.42NA, 20 mm working distance; 50X, 0.42NA, 20 mm working distance). They provide enough working distance to allow the placement of microelectrodes in the specimen.

Optical image hiding based on dual-channel simultaneous phase-shifting interferometry and compressive sensing

2016 ◽  
Vol 123 (1) ◽  
Author(s):  
Jiaosheng Li ◽  
Liyun Zhong ◽  
Qinnan Zhang ◽  
Yunfei Zhou ◽  
Jiaxiang Xiong ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Optical Image ◽  
Phase Shifting ◽  
Phase Shifting Interferometry ◽  
Dual Channel ◽  
Image Hiding

scholarly journals High responsivity in MoS2 phototransistors based on charge trapping HfO2 dielectrics

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Roda Nur ◽  
Takashi Tsuchiya ◽  
Kasidit Toprasertpong ◽  
Kazuya Terabe ◽  
Shinichi Takagi ◽  
...  
Keyword(s):  
Transition Metal Dichalcogenides ◽  
Charge Trapping ◽  
Dual Function ◽  
Light Illumination ◽  
Low Light ◽  
High Responsivity ◽  
Metal Dichalcogenides ◽  
High Resolution Images ◽  
Weak Light ◽  
Light Signals

Abstract2D Transition Metal Dichalcogenides hold a promising potential in future optoelectronic applications due to their high photoresponsivity and tunable band structure for broadband photodetection. In imaging applications, the detection of weak light signals is crucial for creating a better contrast between bright and dark pixels in order to achieve high resolution images. The photogating effect has been previously shown to offer high light sensitivities; however, the key features required to create this as a dominating photoresponse has yet to be discussed. Here, we report high responsivity and high photogain MoS2 phototransistors based on the dual function of HfO2 as a dielectric and charge trapping layer to enhance the photogating effect. As a result, these devices offered a very large responsivity of 1.1 × 106 A W−1, a photogain >109, and a detectivity of 5.6 × 1013 Jones under low light illumination. This work offers a CMOS compatible process and technique to develop highly photosensitive phototransistors for future low-powered imaging applications.

scholarly journals Deep Learning for SAR-Optical Image Matching

2019 ◽  
Author(s):  
Lloyd Haydn Hughes ◽  
Nina Merkle ◽  
Tatjana Burgmann ◽  
Stefan Auer ◽  
Michael Schmitt
Keyword(s):  
Deep Learning ◽  
Image Matching ◽  
Optical Image

scholarly journals AUTOMATIC FISH DETECTION FROM DIFFERENT MARINE ENVIRONMENTS VIDEO USING DEEP LEARNING

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 191-198
Author(s):  
A. Loulidi ◽  
R. Houssa ◽  
L. Buhl-Mortensen ◽  
H. Zidane ◽  
H. Rhinane
Keyword(s):  
Deep Learning ◽  
Fish Species ◽  
Data Augmentation ◽  
Atlantic Coast ◽  
Training Dataset ◽  
Optimization Approach ◽  
Marine Environments ◽  
Low Light ◽  
Fish Detection ◽  
Performance Results

Abstract. The marine environment provides many ecosystems that support habitats biodiversity. Benthic habitats and fish species associations are investigated using underwater gears to secure and manage these marine ecosystems in a sustainable manner. The current study evaluates the possibility of using deep learning methods in particular the You Only Look Once version 3 algorithm to detect fish in different environments such as; different shading, low light, and high noise within images and by each frame within an underwater video, recorded in the Atlantic Coast of Morocco. The training dataset was collected from Open Images Dataset V6, a total of 1295 Fish images were captured and split into a training set and a test set. An optimization approach was applied to the YOLOv3 algorithm which is data augmentation transformation to provide more learning samples. The mean average precision (mAP) metric was applied to measure the YOLOv3 model’s performance. Results of this study revealed with a mAP of 91,3% the proposed method is proved to have the capability of detecting fish species in different natural marine environments also it has the potential to be applied to detect other underwater species and substratum.

scholarly journals Interaction of light with lead halide perovskites: A review

2019 ◽  
Vol 2 (2) ◽  
pp. 67
Author(s):  
Zhiya Dang ◽  
Duc Anh Dinh
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Light Illumination ◽  
Low Light ◽  
Practical Applications ◽  
Halide Perovskites ◽  
Halide Perovskite ◽  
First Time ◽  
Lead Halide ◽  
Sample Environment

Lead halide perovskites are the new rising generation of semiconductor materials due to their unique optical and electrical properties. The investigation of the interaction of halide perovskites and light is a key issue not only for understanding their photophysics but also for practical applications. Hence, tremendous efforts have been devoted to this topic and brunch into two: (i) decomposition of the halide perovskites thin films under light illumination; and (ii) influence of light soaking on their photoluminescence (PL) properties. In this review, we for the first time thoroughly compare the illumination conditions and the sample environment to correlate the PL changes and decomposition of perovskite under light illumination. In the case of vacuum and dry nitrogen, PL of the halide perovskite (MAPbI3–xClx, MAPbBr3–xClx, MAPbI3) thin films decreases due to the defects induced by light illumination, and under high excitations, the thin film even decomposes. In the presence of oxygen or moisture, light induces the PL enhancement of halide perovskite (MAPbI3) thin films at low light illumination, while increasing the excitation, which causes the PL to quench and perovskite thin film to decompose. In the case of mixed halide perovskite ((MA)Pb(BrxI1-x)3) light induces reversible segregation of Br domains and I domains. 

scholarly journals Automated recognition of optical image based potato leaf blight diseases using deep learning

2021 ◽  
pp. 101781
Author(s):  
Kulendu Kashyap Chakraborty ◽  
Rashmi Mukherjee ◽  
Chandan Chakroborty ◽  
Kangkana Bora
Keyword(s):  
Deep Learning ◽  
Leaf Blight ◽  
Optical Image ◽  
Potato Leaf ◽  
Automated Recognition
Sign in / Sign up

Export Citation Format

Share Document