Lightweight image super-resolution with expectation-maximization attention mechanism

In recent years, with the rapid development of deep learning, super-resolution methods based on convolutional neural networks (CNNs) have made great progress. However, the parameters and the required consumption of computing resources of these methods are also increasing to the point that such methods are difficult to implement on devices with low computing power. To address this issue, we propose a lightweight single image super-resolution network with an expectation-maximization attention mechanism (EMASRN) for better balancing performance and applicability. Specifically, a progressive multi-scale feature extraction block (PMSFE) is proposed to extract feature maps of different sizes. Furthermore, we propose an HR-size expectation-maximization attention block (HREMAB) that directly captures the long-range dependencies of HR-size feature maps. We also utilize a feedback network to feed the high-level features of each generation into the next generation’s shallow network. Compared with the existing lightweight single image super-resolution (SISR) methods, our EMASRN reduces the number of parameters by almost one-third. The experimental results demonstrate the superiority of our EMASRN over state-of-the-art lightweight SISR methods in terms of both quantitative metrics and visual quality. The source code can be downloaded at https://github.com/xyzhu1/EMASRN.</div

Cross view capture for stereo image super-resolution

Stereo image super-resolution exploits additional features from cross view image pairs for high resolution (HR) image reconstruction. Recently, several new methods have been proposed to investigate cross view features along epipolar lines to enhance the visual perception of recovered HR images. Despite the impressive performance of these methods, global contextual features from cross view images are left unexplored. In this paper, we propose a cross view capture network (CVCnet) for stereo image super-resolution by using both global contextual and local features extracted from both views. Specifically, we design a cross view block to capture diverse feature embeddings from the views in stereo vision. In addition, a cascaded spatial perception module is proposed to redistribute each location in feature maps according to the weight it occupies to make the extraction of features more effective. Extensive experiments demonstrate that our proposed CVCnet outperforms the state-of-the-art image super-resolution methods to achieve the best performance for stereo image super-resolution tasks. The source code is available at https://github.com/xyzhu1/CVCnet.</p

ReE3D: boosting novel view synthesis for monocular images using residual encoders

In recent years, novel view synthesis from a monocular image has become a research hot-spot that attracts significant attention. Some recent work identifies latent vectors for high-quality view generation via iterative optimisation, which is a time-consuming process. In contrast, some others utilise an encoder learning a mapping function to approximately estimate optimal latent codes, which significantly reduces its processing time but sacrifices reconstruction quality. Consequently, how to balance synthesis quality and its generation efficiency still remains challenging. In this paper, we propose a residual-based encoder to incorporate with a 3D Generative Adversarial Networks (GAN), named ReE3D, for novel view synthesis. It applies an iterative prediction of latent codes to ensure much higher quality of novel view synthesis with an insignificant increase of processing time when compared to existing encoder-based 3D GAN inversion methods. Additionally, we enforce a novel geometric loss constraint on the encoder to predict view-invariant latent codes, thus effectively mitigating the trade-off between geometric and texture quality in 3D GAN inversion. Extensive experimental results demonstrate that our extended encoder-based method has achieved best trade-off performance in terms of novel view synthesis quality and its execution time. Our method has gained comparable synthesis quality with exponentially decreased processing time when compared to iterative optimisation methods, while improved synthesis performance of encoder-based methods significantly.</p

Hepatitis B Virus Stimulated Fibronectin Facilitates Viral Maintenance and Replication through Two Distinct Mechanisms

<div><p>Fibronectin (FN) is a high molecular weight extracellular matrix protein that functions in cell adhesion, growth, migration, and embryonic development. However, little is known about the role of FN during viral infection. In the present study, we found significantly higher levels of FN in sera, and liver tissues from hepatitis B virus (HBV) patients relative to healthy individuals. HBV expression enhanced FN mRNA and protein levels in the hepatic cell lines Huh7 and HepG2. HBV infection of susceptible HepG2-sodium taurocholate co-transporting polypeptide cells also increased FN expression. We also found that transcriptional factor specificity protein 1 was involved in the induction of FN by HBV. Knockdown of FN expression significantly inhibited HBV DNA replication and protein synthesis through activating endogenous IFN-α production. In addition, FN interacted with the transforming growth factor β-activated protein kinase 1 (TAK1) and TAK1-binding protein complex and attenuated interferon signaling by inhibiting TAK1 phosphorylation. Furthermore, the nuclear translocation of NF-κB/p65 was found to be inhibited by FN. We also observed that FN promoted HBV enhancers to support HBV expression. These results suggest novel functions of endogenous FN involved in immune evasion and maintenance of HBV replication.</p></div

HBV promotes FN expression by activating Sp1 activities.

<p>(A) Huh7 cells were transfected with luciferase reporter plasmids containing the FN promoter (pFN-promoter-Luc) along with pHBV or vector. Cell culture medium was refreshed with serum-free medium 6 h post-transfection. Luciferase activity was measured 24 h after serum starvation. (B) Huh7 cells were transfected with wild-type, truncated, or mutated FN promoters along with pHBV and then cell culture medium was refreshed with serum-free medium 6 h post-transfection. Luciferase activity was measured 48 h after serum starvation. (C) Huh7 cells were transfected with empty vector or pHBV and then ChIP analysis was performed to assess Sp1 binding capability to FN promoter 48 h post-transfection. All experiments were repeated at least three times with similar results. Data represent means ± SD, n = 3 (*p<0.05).</p

Birth Characteristics, Ohio 2006–2010.

<p>Dichotomous variables for first 2 columns are presented as percent of total for each category. Stillbirth rate is presented as number of stillbirths per 1000 total births per each category.</p><p>Birth Characteristics, Ohio 2006–2010.</p

PM_2.5 levels in Ohio 2006–2010, by trimester of exposure in pregnancy.

<p>PM_2.5 levels are expressed as mean air concentration in μg/m³. SD = standard deviation, IQR = interquartile range (3^rd, 1^st quartile).</p><p>PM_2.5 levels in Ohio 2006–2010, by trimester of exposure in pregnancy.</p

Flow diagram of the study population, Ohio births 2006–2010.

<p>Flow diagram of the study population, Ohio births 2006–2010.</p

A schematic figure shows the relationship between FN and HBV.

<p>HBV increases FN expression through activation of Sp1. In turn, induced FN facilitates HBV replication and expression via two distinct mechanisms: 1) FN binds to the TAK-TAB complex, inhibits phosphorylation of TAK1 and reduces the nuclear translocation of NF-κB p65 to decrease endogenous IFN-α production; 2) FN activates HBV EII activity by mediating the binding of liver-specific factor HNF-4α.</p

Effect of HBV on FN expression.

<p>(A) The indicated cells were serum starved for 24 h and then FN expression was determined by qRT-PCR and western blot (left and middle panel). Huh7 cells were transfected with vector or pHBV (ayw) and 48 h later, FN expression was determined by qRT-PCR and western blot (right panel). (B) Mock-infected or HBV-infected HepG2-hNTCP cells were analyzed for FN expression 11 days post-infection. (C) HBe, HBs, HBV core protein, and HBV DNA were detected 11 days after infection. N.D., not detected. (D) HBV-infected HepG2-hNTCP cells were analyzed for FN expression at indicated days post-infection. Numbers below the blots are the quantified optical density; control blots were set as 100. All experiments were repeated at least three times with similar results. Data represent means ± SD, n = 3 (*p<0.05).</p