Electron microscopy (EM) image segmentation plays an important role in computer‐aided diagnosis of specific pathogens or disease. However, EM image segmentation is a laborious task and needs to impose experts knowledge, which can take up valuable time from research. Convolutional neural network (CNN)‐based methods have been proposed for EM image segmentation and achieved considerable progress. Among those CNN‐based methods, UNet is regarded as the state‐of‐the‐art method. However, the UNet usually has millions of parameters to increase training difficulty and is limited by the issue of vanishing gradients. To address those problems, the authors present a novel highly parameter efficient method called DenseUNet, which is inspired by the approach that takes particular advantage of recent advances in both UNet and DenseNet. In addition, they successfully apply the weighted loss, which enables us to boost the performance of segmentation. They conduct several comparative experiments on the ISBI 2012 EM dataset. The experimental results show that their method can achieve state‐of‐the‐art results on EM image segmentation without any further post‐processing module or pre‐training. Moreover, due to smart design of the model, their approach has much less parameters than currently published encoder–decoder architecture variants for this dataset.

Recently, deep convolutional neural networks (CNN) have been attracting considerable attention in single image superresolution. Some CNN-based methods, such as VDSR verified that residual learning can speed up the training and significantly improve the performance of accuracy. However, with very deep networks, convergence speed is still a critical issue in training due to the cost of requiring enormous parameters. In order to deal with this issue, we redesign the residual networks based on dilated networks. In this paper, we propose symmetrical dilated residual convolution networks (FDSR) to tackle image superresolution problems. Our network is on the basis of the dilated convolutions supported exponential expansion of the receptive field without loss of resolution and coverage. This means that FDSR can speed up the training and improve the performance of accuracy without increasing the model’s depth or complexity. Meanwhile, we attempt to combine the image pre-processing approach of VGG-net with Mean Squared error (MSE) to enhance the performance. The experimental results demonstrate that the training time-consuming proposed model achieves nearly a half with even superior restoration quality. Further, we present a novel network with less layers and parameters can achieve realtime performance on a generic CPU and still maintain superior performance.

The traditional Frost filter is improved by the adaptive windowing and adaptive tuning factor for the synthetic aperture radar (SAR) images in this letter. The proposed double-adaptive Frost filter simultaneously makes the window size and the tuning factor adaptively adjusted in terms of the regional characteristics, leading to an effective balance between speckle suppression and edge preservation. The despeckling experiments on the simulated and real SAR images demonstrate that in comparison to the Lee filter, the Gamma maximum a posteriori (MAP) filter, the traditional Frost filter, and the Frost filter only with the adaptive tuning factor, the double-adaptive Frost filter sufficiently suppresses the speckle in homogeneous regions and in edge regions and effectively preserves the edges and fine details.

Many very deep neural networks are proposed to obtain accurate super‐resolution reconstruction of remote sensing images. However, the deeper the network for image SR is, the more difficult it is to train. The low‐resolution inputs and features contain abundant low‐frequency information and noise, which are treated equally as the high‐frequency information to across the network. To solve these problems, a novel single‐image super‐resolution algorithm named pre‐training of gated convolution neural network (PGCNN) is proposed for remote sensing images. The proposed PGCNN consists of several residual blocks with long skip connections. Each residual block contains an additional well‐designed gated convolution unit, which provides different weights to high‐frequency information and low‐frequency information to control the transmission of information, making the main network focus on learning high‐frequency information. Compared with several state‐of‐the‐art methods, experimental results on the remote sensing datasets (SIRI‐WHU, NWPU‐RESISC45, RSSCN7 and UC‐Merced‐Land‐Use) show that the proposed PGCNN has the accuracy and visual improvements.