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Reducing noise has always been one of the standard problems of the image analysis and processing community. Often though, at the same time as reducing the noise in a signal, it is important to preserve the edges. Edges are of critical importance to the visual appearance of images. So, it is desirable to preserve important features, such as edges, corners and other sharp structures, during the denoising process. This paper presents a review of some significant work in the area of image denoising. It provides a brief general classification of image denoising methods. The main aim of this survey is to provide evolution of research in the direction of edge-preserving image denoising. It characterizes some of the well known edge-preserving denoising methods, elaborating each of them, and discusses the advantages and drawbacks of each. Basic ideas and improvement of the denoising methods are also comprehensively summarized and analyzed in depth. Often, researchers face difficulty in selecting an appropriate denoising method that is specific to their purpose. We have classified and systemized these denoising methods. The key goal of this paper is to provide researchers with background on a progress of denoising methods so as to make it easier for researchers to choose the method best suited to their aims.

Hierarchical models are extensively studied and widely used in statistics and many other scientific areas. They provide an effective tool for combining information from similar resources and achieving partial pooling of inference. Since the seminal work by James and Stein ( 1961 ) and Stein ( 1962 ), shrinkage estimation has become one major focus for hierarchical models. For the homoscedastic normal model, it is well known that shrinkage estimators, especially the James-Stein estimator, have good risk properties. The heteroscedastic model, though more appropriate for practical applications, is less well studied, and it is unclear what types of shrinkage estimators are superior in terms of the risk. We propose in this article a class of shrinkage estimators based on Stein's unbiased estimate of risk (SURE). We study asymptotic properties of various common estimators as the number of means to be estimated grows (p → ∞). We establish the asymptotic optimality property for the SURE estimators. We then extend our construction to create a class of semiparametric shrinkage estimators and establish corresponding asymptotic optimality results. We emphasize that though the form of our SURE estimators is partially obtained through a normal model at the sampling level, their optimality properties do not heavily depend on such distributional assumptions. We apply the methods to two real datasets and obtain encouraging results.

This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection. Formulated as the minimization of a discrete Mumford–Shah functional and estimated via a theoretically grounded alternating minimization scheme, the bottleneck of such a variational approach lies in the need to fine-tune their hyperparameters, while not having access to ground truth data. To that aim, a Stein-like strategy providing optimal hyperparameters is designed, based on the minimization of an unbiased estimate of the quadratic risk. Efficient and automated minimization of the estimate of the risk crucially relies on an unbiased estimate of the gradient of the risk with respect to hyperparameters. Its practical implementation is performed using a forward differentiation of the alternating scheme minimizing the Mumford–Shah functional, requiring exact differentiation of the proximity operators involved. Intensive numerical experiments are performed on synthetic images with different geometry and noise levels, assessing the accuracy and the robustness of the proposed procedure. The resulting parameter-free piecewise-smooth estimation and contour detection procedure, not requiring prior image processing expertise nor annotated data, can then be applied to real-world images.

The article is concerned with empirical Bayes shrinkage estimators for the heteroscedastic hierarchical normal model using Stein's unbiased estimate of risk (SURE). Recently, Xie, Kou, and Brown proposed a class of estimators for this type of problems and established their asymptotic optimality properties under the assumption of known but unequal variances. In this article, we consider this problem with unequal and unknown variances, which may be more appropriate in real situations. By placing priors for both means and variances, we propose novel SURE-type double shrinkage estimators that shrink both means and variances. Optimal properties for these estimators are derived under certain regularity conditions. Extensive simulation studies are conducted to compare the newly developed methods with other shrinkage techniques. Finally, the methods are applied to the well-known baseball dataset and a gene expression dataset. Supplementary materials for this article are available online.

A data-driven block thresholding procedure for wavelet regression is proposed and its theoretical and numerical properties are investigated. The procedure empirically chooses the block size and threshold level at each resolution level by minimizing Stein's unbiased risk estimate. The estimator is sharp adaptive over a class of Besov bodies and achieves simultaneously within a small constant factor of the minimax risk over a wide collection of Besov Bodies including both the \"dense\" and \"sparse\" cases. The procedure is easy to implement. Numerical results show that it has superior finite sample performance in comparison to the other leading wavelet thresholding estimators.

We estimated the case-fatality risk for coronavirus disease cases in China (3.5%); China, excluding Hubei Province (0.8%); 82 countries, territories, and areas (4.2%); and on a cruise ship (0.6%). Lower estimates might be closest to the true value, but a broad range of 0.25%-3.0% probably should be considered.

•Pregnancy is considered an important risk factor for severe outcomes from influenza virus infection.•The World Health Organization recommends that pregnant women be prioritized for vaccination.•No comprehensive systematic review supporting this recommendation has been conducted to date.•We found a higher risk for hospital admission following influenza but found no increase in mortality or other outcomes.•Comparative, population-based studies are needed to best evaluate the attributable risk of pregnancy. Pregnancy is considered to be an important risk factor for severe complications following influenza virus infection. As a consequence, WHO recommendations prioritize pregnant women over other risk groups for influenza vaccination. However, the risk associated with pregnancy has not been systematically quantified. Systematic review and meta-analysis of observational studies that reported on pregnancy as a risk factor for severe outcomes from influenza virus infection. MEDLINE, EMBASE, CINAHL, and CENTRAL up to April 2014. Studies reporting on outcomes in pregnant women with influenza in comparison to non-pregnant patients with influenza. Outcomes included community-acquired pneumonia, hospitalization, admission to intensive care units (ICU), ventilatory support, and death. Two reviewers conducted independent screening and data extraction. A random effects model was used to obtain risk estimates. Ecological studies were summarized descriptively. A total of 142 non-ecological and 10 ecological studies were included. The majority of studies (n=136, 95.8%) were conducted during the 2009 influenza A (pH1N1) pandemic. There was a higher risk for hospitalization in pregnant versus non-pregnant patients infected with influenza (odds ratio [OR] 2.44, 95% CI 1.22–4.87), but no significant difference in mortality (OR 1.04, 95% CI 0.81–1.33) or other outcomes. Ecologic studies confirmed the association between hospitalization risk and pregnancy and 4 of 7 studies reported higher mortality rates in pregnant women. No studies were identified in which follow-up began prior to contact with the healthcare system and lack of adjustment for confounding factors. We found that influenza during pregnancy resulted in a higher risk of hospital admission than influenza infection in non-pregnant individuals, but that the risk of mortality following influenza was similar in both pregnant and non-pregnant individuals.

To examine and quantify the potential dose-response relationship between red and processed meat consumption and risk of all-cause, cardiovascular and cancer mortality. We searched MEDLINE, Embase, ISI Web of Knowledge, CINHAL, Scopus, the Cochrane library and reference lists of retrieved articles up to 30 November 2014 without language restrictions. We retrieved prospective cohort studies that reported risk estimates for all-cause, cardiovascular and cancer mortality by red and/or processed meat intake levels. The dose-response relationships were estimated using data from red and processed meat intake categories in each study. Random-effects models were used to calculate pooled relative risks and 95 % confidence intervals and to incorporate between-study variations. Nine articles with seventeen prospective cohorts were eligible in this meta-analysis, including a total of 150 328 deaths. There was evidence of a non-linear association between processed meat consumption and risk of all-cause and cardiovascular mortality, but not for cancer mortality. For processed meat, the pooled relative risk with an increase of one serving per day was 1·15 (95 % CI 1·11, 1·19) for all-cause mortality (five studies; P<0·001 for linear trend), 1·15 (95 % CI 1·07, 1·24) for cardiovascular mortality (six studies; P<0·001) and 1·08 (95 % CI 1·06, 1·11) for cancer mortality (five studies; P<0·001). Similar associations were found with total meat intake. The association between unprocessed red meat consumption and mortality risk was found in the US populations, but not in European or Asian populations. The present meta-analysis indicates that higher consumption of total red meat and processed meat is associated with an increased risk of total, cardiovascular and cancer mortality.

Casing and cement impairment in oil and gas wells can lead to methane migration into the atmosphere and/or into underground sources of drinking water. An analysis of 75,505 compliance reports for 41,381 conventional and unconventional oil and gas wells in Pennsylvania drilled from January 1, 2000—December 31, 2012, was performed with the objective of determining complete and accurate statistics of casing and cement impairment. Statewide data show a sixfold higher incidence of cement and/or casing issues for shale gas wells relative to conventional wells. The Cox proportional hazards model was used to estimate risk of impairment based on existing data. The model identified both temporal and geographic differences in risk. For post-2009 drilled wells, risk of a cement/casing impairment is 1.57-fold [95% confidence interval (CI) (1.45, 1.67); P < 0.0001] higher in an unconventional gas well relative to a conventional well drilled within the same time period. Temporal differences between well types were also observed and may reflect more thorough inspections and greater emphasis on finding well leaks, more detailed note taking in the available inspection reports, or real changes in rates of structural integrity loss due to rushed development or other unknown factors. Unconventional gas wells in northeastern (NE) Pennsylvania are at a 2.7-fold higher risk relative to the conventional wells in the same area. The predicted cumulative risk for all wells (unconventional and conventional) in the NE region is 8.5-fold [95% CI (7.16, 10.18); P < 0.0001] greater than that of wells drilled in the rest of the state.

An association between processed and red meat consumption and total mortality has been reported by epidemiological studies; however, there are many controversial reports regarding the association between meat consumption and CVD and IHD mortality. The present meta-analysis was carried out to summarise the evidence from prospective cohort studies on the association between consumption of meat (total, red, white and processed) and all-cause, CVD and IHD mortality. Cohort studies were identified by searching the PubMed and ISI Web of Knowledge databases. Risk estimates for the highest v. the lowest consumption category and dose–response meta-analysis were calculated using a random-effects model. Heterogeneity among the studies was also evaluated. A total of thirteen cohort studies were identified (1 674 272 individuals). Subjects in the highest category of processed meat consumption had 22 and 18 % higher risk of mortality from any cause and CVD, respectively. Red meat consumption was found to be associated with a 16 % higher risk of CVD mortality, while no association was found for total and white meat consumption. In the dose–response meta-analysis, an increase of 50 g/d in processed meat intake was found to be positively associated with all-cause and CVD mortality, while an increase of 100 g/d in red meat intake was found to be positively associated with CVD mortality. No significant associations were observed between consumption of any type of meat and IHD mortality. The results of the present meta-analysis indicate that processed meat consumption could increase the risk of mortality from any cause and CVD, while red meat consumption is positively but weakly associated with CVD mortality. These results should be interpreted with caution due to the high heterogeneity observed in most of the analyses as well as the possibility of residual confounding.