Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising

Video denoising aims at removing noise from videos to recover clean ones. Some existing works show that optical flow can help the denoising by exploiting the additional spatial-temporal clues from nearby frames. However, the flow estimation itself is also sensitive to noise, and can be unusable under large noise levels. To this end, we propose a new multi-scale refined optical flow-guided video denoising method, which is more robust to different noise levels. Our method mainly consists of a denoising-oriented flow refinement (DFR) module and a flow-guided mutual denoising propagation (FMDP) module. Unlike previous works that directly use off-the-shelf flow solutions, DFR first learns robust multi-scale optical flows, and FMDP makes use of the flow guidance by progressively introducing and refining more flow information from low resolution to high resolution. Together with real noise degradation synthesis, the proposed multi-scale flow-guided denoising network achieves state-of-the-art performance on both synthetic Gaussian denoising and real video denoising. The codes will be made publicly available

Towards Interpretable Video Super-Resolution via Alternating Optimization

In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low-resolution blurry video. Such problem often occurs when recording a fast dynamic event with a low-framerate and low-resolution camera, and the captured video would suffer from three typical issues: i) motion blur occurs due to object/camera motions during exposure time; ii) motion aliasing is unavoidable when the event temporal frequency exceeds the Nyquist limit of temporal sampling; iii) high-frequency details are lost because of the low spatial sampling rate. These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences. To address this, we propose an interpretable STVSR framework by leveraging both model-based and learning-based methods. Specifically, we formulate STVSR as a joint video deblurring, frame interpolation, and super-resolution problem, and solve it as two sub-problems in an alternate way. For the first sub-problem, we derive an interpretable analytical solution and use it as a Fourier data transform layer. Then, we propose a recurrent video enhancement layer for the second sub-problem to further recover high-frequency details. Extensive experiments demonstrate the superiority of our method in terms of quantitative metrics and visual quality.Comment: ECCV 202

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved. In this paper, we attempt to solve this problem from the perspective of network architecture design and training data synthesis. Specifically, for the network architecture design, we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, and then plug it as the main building block into the widely-used image-to-image translation UNet architecture. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and processed camera sensor noises) and resizing, and also involves a random shuffle strategy and a double degradation strategy. Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability. We believe our work can provide useful insights into current denoising research.Comment: Codes: https://github.com/cszn/SCUNe

Isolation and complete genomic characterization of H1N1 subtype swine influenza viruses in southern China through the 2009 pandemic

<p>Abstract</p> <p>Background</p> <p>The swine influenza (SI) is an infectious disease of swine and human. The novel swine-origin influenza A (H1N1) that emerged from April 2009 in Mexico spread rapidly and caused a human pandemic globally. To determine whether the tremendous virus had existed in or transmitted to pigs in southern China, eight H1N1 influenza strains were identified from pigs of Guangdong province during 2008-2009.</p> <p>Results</p> <p>Based on the homology and phylogenetic analyses of the nucleotide sequences of each gene segments, the isolates were confirmed to belong to the classical SI group, with HA, NP and NS most similar to 2009 human-like H1N1 influenza virus lineages. All of the eight strains were low pathogenic influenza viruses, had the same host range, and not sensitive to class of antiviral drugs.</p> <p>Conclusions</p> <p>This study provides the evidence that there is no 2009 H1N1-like virus emerged in southern China, but the importance of swine influenza virus surveillance in China should be given a high priority.</p

High speed self-testing quantum random number generation without detection loophole

Quantum mechanics provides means of generating genuine randomness that is impossible with deterministic classical processes. Remarkably, the unpredictability of randomness can be certified in a self-testing manner that is independent of implementation devices. Here, we present an experimental demonstration of self-testing quantum random number generation based on an detection-loophole free Bell test with entangled photons. In the randomness analysis, without the assumption of independent identical distribution, we consider the worst case scenario that the adversary launches the most powerful attacks against quantum adversary. After considering statistical fluctuations and applying an 80 Gb $\times$ 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than $10^{-5}$. Such self-testing random number generators mark a critical step towards realistic applications in cryptography and fundamental physics tests.Comment: 34 pages, 10 figure

The combination of deoxynivalenol and zearalenone at permitted feed concentrations causes serious physiological effects in young pigs

This study was to investigate the effects of the combination of deoxynivalenol (DON) and zearalenone (ZON) on pigs. Twenty-four weaning piglets were divided into a control group fed a diet free of mycotoxins and a toxin group fed a diet containing 1 mg/kg DON and 250 µg/kg ZON. The results showed that supplementation of DON and ZON in diets had extensive effects on pigs. More specifically, DON and ZON caused levels of total protein, albumin, and globulin in sera to decrease (p < 0.05) by 14.5%, 6.5% and 11.3%, respectively, and at the same time increased (p < 0.05) the serum enzyme activities of γ-glutamyltransferase, aspartate aminotransferase and alanine aminotransferase by 72.0%, 32.6% and 36.6%, respectively. In addition, DON and ZON decreased (p < 0.05) the level of anti-classical swine fever antibody titers by 14.8%. Real-time PCR showed that DON and ZON caused the mRNA expression levels of IFN-γ, TNF-α, IL-2, to decrease (p < 0.05) by 36.0%, 29.0% and 35.4%, respectively. Histopathological studies demonstrated that DON and ZON caused abnormalities in the liver, spleen, lymph nodes, uterus, and kidney. The concentrations of DON and ZON used in this study are in line with the published critical values permitted by BML. Our study clearly put the standard and adequacy of safety measures for these toxins into question. The authors suggest that with the increasing availability of cellular and molecular technologies, it is time to revisit the safety standards for toxins in feeds so as to make feeds safer, providing consumers with safer products

Heritable Targeted Inactivation of Myostatin Gene in Yellow Catfish (Pelteobagrus fulvidraco) Using Engineered Zinc Finger Nucleases

Yellow catfish (Pelteobagrus fulvidraco) is one of the most important freshwater aquaculture species in China. However, its small size and lower meat yield limit its edible value. Myostatin (MSTN) is a negative regulator of mammalian muscle growth. But, the function of Mstn in fish remains elusive. To explore roles of mstn gene in fish growth and create a strain of yellow catfish with high amount of muscle mass, we performed targeted disruption of mstn in yellow catfish using engineered zinc-finger nucleases (ZFNs). Employing zebrafish embryos as a screening system to identify ZFN activity, we obtained one pair of ZFNs that can edit mstn in yellow catfish genome. Using the ZFNs, we successfully obtained two founders (Founder July29-7 and Founder July29-8) carrying mutated mstn gene in their germ cells. The mutated mstn allele inherited from Founder July29-7 was a null allele (mstnnju6) containing a 4 bp insertion, predicted to encode function null Mstn. The mutated mstn inherited from Founder July29-8 was a complex type of mutation (mstnnju7), predicted to encode a protein lacking two amino acids in the N-terminal secretory signal of Mstn. Totally, we obtained 6 mstnnju6/+ and 14 mstnnju7/+ yellow catfish. To our best knowledge, this is the first endogenous gene knockout in aquaculture fish. Our result will help in understanding the roles of mstn gene in fish

Glass forming ability, thermal stability and elastic properties of Zr-Ti-Cu-Be-(Fe) bulk metallic glasses

A series of Zr35−xTi30Cu7.5Be27.5Fex (x = 0–10) bulk metallic glasses are cast using water-cooled copper mold casting technique. The glass forming ability (GFA) and thermal stability of Zr35−xTi30Cu7.5Be27.5Fex alloys were studied by means of X-ray diffraction and differential scanning calorimetry. It was found that the Zr35−xTi30Cu7.5Be27.5Fex (x = 2, 3, 5 and 7, respectively) alloys could be cast into glassy cylindrical rods with a diameters up to 20 mm. However, higher Fe content (x = 10) deteriorates the GFA remarkably. The supercooled liquid region (ΔTx), γ parameter (defined as Tx/(Tg + Tl)) and the reduced glass transition temperature Trg (defined as Tg/Tl) are employed to evaluate the GFA in Zr35−xTi30Cu7.5Be27.5Fex (x = 0–10) alloys. The results showed that Trg was more effective in gauging the GFA of the Zr35−xTi30Cu7.5Be27.5Fex alloy systems than ΔTx and γ parameter. In addition, elastic constant (Poisson’s ratio) was also employed as a gauge to evaluate the glass forming ability in Zr35−xTi30Cu7.5Be27.5Fex alloys. Keywords: Zr-based alloys, Bulk metallic glasses, Glass forming ability, Thermal stability and elastic propertie