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• We make the first attempt to incorporate the deep denoiser prior for infrared small target detection. Through the plug-and-play framework, the proposed method can better preserve fine image details and remove noise. • Considering that different singular values have different importance, we use the weighted sum of weighted tensor nuclear norm for more accurate background estimation. • An efficient algorithm is developed for solving the proposed problem based on alternating direction multiplier method. Many existing low-rank methods have achieved good detection performance in uniform scenes, but they suffer from a high false alarm rate in complex noisy scenes. Therefore, it is important to improve the detection performance of low-rank models in noisy scenes. In this paper, we first formulate an implicit regularizer by plugging a denoising neural network (termed as deep denoiser), which can learn deep image priors from a large number of natural images. Then, we use the weighted sum of weighted tensor nuclear norm for more accurate background estimation. Finally, alternating direction multiplier method is used to solve the model under the plug-and-play framework. By integrating low-rank prior with deep denoiser prior, our model achieves higher accuracy. Experiments on different scenes demonstrate that our method achieves an improved performance in terms of visual effects and quantitative metrics. Specially, the overall accuracy of AUC value (AU C OA) achieved by the proposed method on Sequences 1-6 are 1.24 % , 1.16 % , 0.63 % , 1.9 % , 0.82 % , 2.06 % higher than those achieved by the second top performing methods, respectively. [ABSTRACT FROM AUTHOR]

The convolutional neural network is the most widely used deep neural network. However, it still has some disadvantages. First, the back-propagation method is usually used in the training of convolutional neural networks, but it has several inherent defects, such as the vanishing gradient problem and exploding gradient problem. Moreover, the training of a convolutional neural network often needs substantial computational resources and time. To solve the above problems, based on pseudoinverse learning (PIL), kernel pseudoinverse learning (KPIL) is proposed, showing improved performance. The number of hidden layer neurons of KPIL is equal to the number of input data and does not need to be set. KPIL uses the kernel method to calculate the output of hidden layer, avoiding the uncertainty of random input weights. Based on KPIL, a deep convolutional kernel pseudoinverse learner with a multi-filter design is proposed, named kernel pseudoinverse learning convolutional neural network (KPIL-CNN), which uses multiple fixed convolutional kernels: kernel pseudoinverse learning filters (KPIL filter), Gabor filters, and random filters. The features obtained by this multi-filter approach are combined into feature maps by learning weights. The experimental results show that the proposed KPIL-CNN performs better than other network models with the same scale in the task of natural image classification. The training of KPIL-CNN is efficient and effective. Furthermore, KPIL-CNN outperforms the existing methods in the classification of the ISIC 2017 skin lesion dataset. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal trends of temperature in the context of global warming is significant for public health. Although many studies have examined changes in temperature and the impacts on human health over the past few decades in many regions, they have often been carried out in data-rich regions and have rarely considered acclimatization explicitly. The most frequent temperature (MFT) indicator provides us with the ability to solve this problem. MFT is defined as the longest period of temperature throughout the year to which a human is exposed and therefore acclimates. In this study, we propose a new method to estimate the number of heat exposure days from the perspective of temperature distribution and MFT, based on the daily mean temperature readings of 2142 weather stations in eight major climate zones in China over the past 20 years. This method can be used to calculate the number of heat exposure days in terms of heat-related mortality risk without the need for mortality data. We estimated the distribution and changes of annual mean temperature (AMT), minimum mortality temperature (MMT), and the number of heat exposure days in different climate zones in China. The AMT, MMT, and number of heat exposure days vary considerably across China. They all tend to decrease gradually from low to high latitudes. Heat exposure days are closely related to the risk of heat-related mortality. In addition, we utilized multiple linear regression (MLR) to analyze the association between the risk of heat-related mortality and the city and its climatic characteristics. Results showed that the number of heat exposure days, GDP per capita, urban population ratio, proportion of elderly population, and climate zone were found to modify the estimate on heat effect, with an R2 of 0.71. These findings will be helpful for the creation of public policies protecting against high-temperature-induced mortalities. [ABSTRACT FROM AUTHOR]