MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images.

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.

Neural Network-Based Ultra-High-Definition Video Live Streaming Optimization Algorithm

Online live streaming has been widely used in distant teaching, online live shopping, and so on. Particularly, online teaching live streaming breaks the time and space boundary of teaching and has better interactivity, which is a new distant education mode. As a new online sales model, online live shopping promotes the rapid development of Internet economy. However, the quality of live video affects the user experience. This paper studies the optimization algorithm of ultra-high-definition live streaming, focusing on superresolution technology. Convolutional neural network (CNN) is a multilayer artificial neural network designed to process two-dimensional input data. It takes advantage of CNN in image processing. This paper proposes an image superresolution algorithm based on hybrid dilated convolution and Laplacian pyramid. By mixing the dilated convolution module, the receptive field of the network can be improved more effectively to obtain more context information so that the high-frequency features of the image can be extracted more effectively. Experiment was running on Set5, Set14, Urban100, and BSD100 datasets, and the results reveal that the proposed algorithm outperforms baselines with respect to peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), and image quality.