Multiple articles have confirmed that an imbalance of the intestinal microbiota is closely related to aberrant immune responses of the intestines and to the pathogenesis of inflammatory bowel diseases (IBDs).

Video super-resolution is a challenging task, which has attracted great attention in research and industry communities. In this paper, we propose a novel end-to-end architecture, called Residual Invertible Spatio-Temporal Network (RISTN) for video super-resolution. The RISTN can sufficiently exploit the spatial information from low-resolution to high-resolution, and effectively models the temporal consistency from consecutive video frames. Compared with existing recurrent convolutional network based approaches, RISTN is much deeper but more efficient. It consists of three major components: In the spatial component, a lightweight residual invertible block is designed to reduce information loss during feature transformation and provide robust feature representations. In the temporal component, a novel recurrent convolutional model with residual dense connections is proposed to construct deeper network and avoid feature degradation. In the reconstruction component, a new fusion method based on the sparse strategy is proposed to integrate the spatial and temporal features. Experiments on public benchmark datasets demonstrate that RISTN outperforms the state-ofthe-art methods.

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Recently, image super‐resolution works based on Convolutional Neural Networks (CNNs) and Generative Adversarial Nets (GANs) have shown promising performance. However, these methods tend to generate blurry and over‐smoothed super‐resolved (SR) images, due to the incomplete loss function and powerless architectures of networks. In this paper, a novel generative adversarial image super‐resolution through deep dense skip connections (GSR‐DDNet), is proposed to solve the above‐mentioned problems. It aims to take advantage of GAN's ability of modeling data distributions, so that GSR‐DDNet can select informative feature representation and model the mapping across the low‐quality and high‐quality images in an adversarial way. The pipeline of the proposed method consists of three main components: 1) The generator of a novel dense skip connection network with the deep structure for learning robust mapping function is proposed to generate SR images from low‐resolution images; 2) The feature extraction network based on VGG‐19 is adopted to capture high frequency feature maps for content loss; and 3) The discriminator with Wasserstein distance is adopted to identify the overall style of SR and ground‐truth images. Experiments conducted on four publicly available datasets demonstrate the superiority against the state‐of‐the‐art methods.