Americas, Special Report, US

[AAqoxbt]

Some users say e-cigs have helped reduce their "smoker's cough," sharpened their senses of taste and smell, and even improved their sleep. The electronic cigarette was invented by Chinese pharmacist Hon Lik, who patented the device in 2003 and introduced it to the Chinese market the following year. Numerous companies are now selling e-cigarettes to customers around the world. But as e-cigarette smoking -- or "vaping" as it's sometimes called -- has grown in popularity, some have concerns about its safety, including the possibility that the vapor created by the devices contains dangerous chemicals. Is the electronic cigarette a cleaner, healthier choice for smokers? Or is it a dangerous device with hidden risks? Both viewpoints have their merits, but on the next page we'll start with the basics: how the product works, and why it's popular. Lighting a traditional cigarette causes the tobacco to burn, releasing smoke that contains nicotine. The user breathes in the smoke to deliver nicotine to the lungs.|Existing deep learning-based video super-resolution (SR) methods usually depend on the supervised learning approach, where the training data is usually generated by the blurring operation with known or predefined kernels (e.g., Bicubic kernel) followed by a decimation operation. However, this does not hold for real applications as the degradation process is complex and cannot be approximated by these idea cases well. Moreover, obtaining high-resolution (HR) videos and the corresponding low-resolution (LR) ones in real-world scenarios is difficult. To overcome these problems, we propose a self-supervised learning method to solve the blind video SR problem, which simultaneously estimates blur kernels and HR videos from the LR videos. As directly using LR videos as supervision usually leads to trivial solutions, we develop a simple and effective method to generate auxiliary paired data from original LR videos according to the image formation of video SR, so that the networks can be better constrained by the generated paired data for both blur kernel estimation and latent HR video restoration.




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