Fast Video Visual Quality and Resolution Improvement using SR-UNet

2021 ◽  
Author(s):  
Federico Vaccaro ◽  
Marco Bertini ◽  
Tiberio Uricchio ◽  
Alberto Del Bimbo
Keyword(s):  
Visual Quality ◽  
Resolution Improvement

Device-Aware Visual Quality Adaptation for Wireless N-Screen Multicast Systems

2013 ◽  
Vol E96.B (12) ◽  
pp. 3181-3189 ◽  
Author(s):  
Inwoong LEE ◽  
Jincheol PARK ◽  
Seonghyun KIM ◽  
Taegeun OH ◽  
Sanghoon LEE
Keyword(s):  
Visual Quality ◽  
Quality Adaptation

Detection of Malicious Spatial-Domain Steganography over Noisy Channels Using Convolutional Neural Networks

2020 ◽  
Vol 2020 (4) ◽  
pp. 76-1-76-7
Author(s):  
Swaroop Shankar Prasad ◽  
Ofer Hadar ◽  
Ilia Polian
Keyword(s):  
State Of The Art ◽  
Visual Quality ◽  
Channel Noise ◽  
Detection Accuracy ◽  
Noisy Channel ◽  
Noisy Channels ◽  
Reliable Transmission ◽  
Reliable Detection ◽  
Natural Noise ◽  
Will Force

Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.

Picosecond Single-Photon and Femtosecond Two-Photon Pulsed Laser Stimulation for IC Characterization and Failure Analysis

2009 ◽  
Author(s):  
V. Pouget ◽  
E. Faraud ◽  
K. Shao ◽  
S. Jonathas ◽  
D. Horain ◽  
...  
Keyword(s):  
Femtosecond Laser ◽  
Single Photon ◽  
Pulsed Laser ◽  
Laser Pulses ◽  
Femtosecond Laser Pulses ◽  
Ultrashort Laser Pulses ◽  
Laser Stimulation ◽  
Two Photon ◽  
Resolution Improvement ◽  
Ultrashort Laser

Abstract This paper presents the use of pulsed laser stimulation with picosecond and femtosecond laser pulses. We first discuss the resolution improvement that can be expected when using ultrashort laser pulses. Two case studies are then presented to illustrate the possibilities of the pulsed laser photoelectric stimulation in picosecond single-photon and femtosecond two-photon modes.

Enhancing Visual Quality of Multi-view 360 Video Compression Pipeline

2019 ◽  
pp. 135
Author(s):  
Junyoung Yun ◽  
Hong-Chang Shin ◽  
Gwangsoon Lee ◽  
Jong-Il Park
Keyword(s):  
Video Compression ◽  
Visual Quality

scholarly journals Comparison of trifocal or hybrid multifocal-extended depth of focus intraocular lenses: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yueyang Zhong ◽  
Kai Wang ◽  
Xiaoning Yu ◽  
Xin Liu ◽  
Ke Yao
Keyword(s):  
Systematic Review ◽  
Visual Acuity ◽  
Meta Analysis ◽  
Visual Quality ◽  
Intraocular Lenses ◽  
Depth Of Focus ◽  
Refractive Lens Exchange ◽  
Extended Depth Of Focus ◽  
Refractive Lens ◽  
Near Visual Acuity

AbstractThis meta-analysis aimed to evaluate the clinical outcomes following implantation of trifocal intraocular lenses (IOLs) or a hybrid multifocal-extended depth of focus (EDOF) IOL in cataract or refractive lens exchange surgeries. We examined 13 comparative studies with bilateral implantation of trifocal (898 eyes) or hybrid multifocal-EDOF (624 eyes) IOLs published through 1 March 2020. Better uncorrected and corrected near visual acuity (VA) were observed in the trifocal group (MD: − 0.143, 95% CI: − 0.192 to − 0.010, P < 0.001 and MD: − 0.149, 95% CI: − 0.217 to − 0.082, P < 0.001, respectively), while the hybrid multifocal-EDOF group presented better uncorrected intermediate VA (MD: 0.055, 95% CI: 0.016 to 0.093, P = 0.005). Trifocal IOLs were more likely to achieve spectacle independence at near distance (RR: 1.103, 95% CI: 1.036 to 1.152, P = 0.002). The halo photic effect was generated more frequently by the trifocal IOLs (RR: 1.318, 95% CI: 1.025 to 1.696, P = 0.031). Contrast sensitivity and subjective visual quality yielded comparable results between groups. Trifocal IOLs demonstrated better performance at near distance but apparently led to more photic disturbances. Our findings provided the most up-to-date and comprehensive evidence by comparing the benefits of advanced IOLs in clinical practice.

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

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