Rain Streaks Removal for Single Image via Kernel-Guided Convolutional Neural Network.

Recently emerged deep learning methods have achieved great success in single image rain streaks removal. However, existing methods ignore an essential factor in the rain streaks generation mechanism, i.e., the motion blur leading to the line pattern appearances. Thus, they generally produce overderaining or underderaining results. In this article, inspired by the generation mechanism, we propose a novel rain streaks removal framework using a kernel-guided convolutional neural network (KGCNN), achieving state-of-the-art performance with a simple network architecture. More precisely, our framework consists of three steps. First, we learn the motion blur kernel by a plain neural network, termed parameter network, from the detail layer of a rainy patch. Then, we stretch the learned motion blur kernel into a degradation map with the same spatial size as the rainy patch. Finally, we use the stretched degradation map together with the detail patches to train a deraining network with a typical ResNet architecture, which produces the rain streaks with the guidance of the learned motion blur kernel. Experiments conducted on extensive synthetic and real data demonstrate the effectiveness of the proposed KGCNN, in terms of rain streaks removal and image detail preservation.

Association between alcohol intake and the risk of pancreatic cancer: a dose-response meta-analysis of cohort studies.

BACKGROUND: Studies examining the association between alcohol intake and the risk of pancreatic cancer have given inconsistent results. The purpose of this study was to summarize and examine the evidence regarding the association between alcohol intake and pancreatic cancer risk based on results from prospective cohort studies. METHODS: We searched electronic databases consisting of PubMed, Ovid, Embase, and the Cochrane Library identifying studies published up to Aug 2015. Only prospective studies that reported effect estimates with 95% confidence intervals (CIs) for the risk of pancreatic cancer, examining different alcohol intake categories compared with a low alcohol intake category were included. Results of individual studies were pooled using a random-effects model. RESULTS: We included 19 prospective studies (21 cohorts) reporting data from 4,211,129 individuals. Low-to-moderate alcohol intake had little or no effect on the risk of pancreatic cancer. High alcohol intake was associated with an increased risk of pancreatic cancer (risk ratio [RR], 1.15; 95% CI: 1.06-1.25). Pooled analysis also showed that high liquor intake was associated with an increased risk of pancreatic cancer (RR, 1.43; 95% CI: 1.17-1.74). Subgroup analyses suggested that high alcohol intake was associated with an increased risk of pancreatic cancer in North America, when the duration of follow-up was greater than 10 years, in studies scored as high quality, and in studies with adjustments for smoking status, body mass index, diabetes mellitus, and energy intake.. CONCLUSIONS: Low-to-moderate alcohol intake was not significantly associated with the risk of pancreatic cancer, whereas high alcohol intake was associated with an increased risk of pancreatic cancer. Furthermore, liquor intake in particular was associated with an increased risk of pancreatic cancer.