The semantic segmentation of high-resolution remote sensing images (HRRSIs) is a basic task for remote sensing image processing and has a wide range of applications. However, the abundant texture information and wide imaging range of HRRSIs lead to the complex distribution of ground objects and unclear boundaries, which bring huge challenges to the segmentation of HRRSIs. To solve this problem, in this paper we propose an improved squeeze and excitation residual network (SERNet), which integrates several squeeze and excitation residual modules (SERMs) and a refine attention module (RAM). The SERM can recalibrate feature responses adaptively by modeling the long-range dependencies in the channel and spatial dimensions, which enables effective information to be transmitted between the shallow and deep layers. The RAM pays attention to global features that are beneficial to segmentation results. Furthermore, the ISPRS datasets were processed to focus on the segmentation of vegetation categories and introduce Digital Surface Model (DSM) images to learn and integrate features to improve the segmentation accuracy of surface vegetation, which has certain prospects in the field of forestry applications. We conduct a set of comparative experiments on ISPRS Vaihingen and Potsdam datasets. The results verify the superior performance of the proposed SERNet.

In this paper, we study the semi-supervised semantic segmentation problem via limited labeled samples and a large number of unlabeled samples. We propose a self-learning semi-supervised approach for the semantic segmentation of high-resolution remote sensing images. Our approach uses two networks (UNet and DeepLabV3) to predict the labels of the same unlabeled sample, and the pseudo labels samples with high prediction consistency are added to the training samples to improve the accuracy of semantic segmentation under the condition of limited labeled samples. Our method expands training data samples by using unlabeled data samples with pseudo labels. In order to verify the effectiveness of the proposed method, some experiments were conducted on the improved ISPRS Vaihingen 2D Semantic Labeling dataset using the method that we proposed. We focus on the extraction of forest and vegetation information and focus on the impact of a large number of unlabeled samples on the precision of semantic segmentation, we combine water, surface, buildings, cars, and background into one category and named others, and we call the changed dataset the improved ISPRS Vaihingen dataset. The experimental results show that the proposed method can effectively improve the semantic segmentation accuracy of high-scoring remote sensing images with limited samples than common deep semi-supervised learning.

The hybridoma cell screening method is usually done manually by human eyes during the production process for monoclonal antibody drugs. This traditional screening method has certain limitations, such as low efficiency and subjectivity bias. Furthermore, most of the existing deep learning-based image segmentation methods have certain drawbacks, due to different shapes of hybridoma cells and uneven location distribution. In this paper, we propose a deep hybridoma cell image segmentation method based on residual and attention U-Net (RA-UNet). Firstly, the feature maps of the five modules in the network encoder are used for multi-scale feature fusion in a feature pyramid form and then spliced into the network decoder to enrich the semantic level of the feature maps in the decoder. Secondly, a dual attention mechanism module based on global and channel attention mechanisms is presented. The global attention mechanism (non-local neural network) is connected to the network decoder to expand the receptive field of the feature map and bring more rich information to the network. Then, the channel attention mechanism SENet (the squeeze-and-excitation network) is connected to the non-local attention mechanism. Consequently, the important features are enhanced by the learning of the feature channel weights, and the secondary features are suppressed, hence improving the cell segmentation performance and accuracy. Finally, the focal loss function is used to guide the network to learn the hard-to-classify cell categories. Furthermore, we evaluate the performance of the proposed RA-UNet method on a newly established hybridoma cell image dataset. Experimental results show that the proposed method has good reliability and improves the efficiency of hybridoma cell segmentation compared with state-of-the-art networks such as FCN, UNet, and UNet++. The results show that the proposed RA-UNet model has improvements of 0.8937%, 0.9926%, 0.9512%, and 0.9007% in terms of the dice coefficients, PA, MPA, and MIoU, respectively.

Low-light images captured at night often suffer from improper exposure, color distortion, and noise, which degrades the image quality and have a negative influence on subsequent applications. Many existing deep learning-based methods enhance low-light images through spatial domain, which may sacrifice the original image information. In this paper, we put forward a deep learning network for enhancing low-light images based on wavelet transform. We utilize the wavelet transform to divide the image into various frequency scales and then analyze the frequency characteristics of different low-light images in the wavelet domain. The proposed network comprises a low-frequency restoration subnet and high-frequency reconstruction subnet that uses an optimal coefficient of wavelet decomposition to construct a frequency pyramid. Furthermore, we utilized different attention mechanisms to extract frequency information from different images, gradually restoring the brightness information and details of low-light images. Additionally, we utilized a self-constructed multi-scale exposure low-light image dataset for training. Numerous experiments on publicly accessible datasets and our established dataset show that the proposed approach quantitatively and qualitatively surpasses state-of-the-art approaches, particularly for real and complex low-light scenarios. Furthermore, our method produces better visual effects than others and performs well in real-time and real-word downstream vision tasks.