The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme.

Due to the uneven distribution and large scale change of sheep in the pasture, it is not conducive to the counting and statistics of sheep in animal husbandry. The traditional target counting algorithm has low counting accuracy in the field of animal husbandry, and there are fewer sheep data sets for research. To solve these problems, the data set of sheep density estimation was established, and a method of grassland sheep number estimation based on multi-scale residual visual information fusion Network (MRVIFNet) was proposed. This method extracts multi-scale features of sheep targets by using multiple parallel hole convolutions with different hole rates, and designs a depth neural network that is more suitable for live counting of sheep, so as to reduce the grid effect caused by hole convolution and better adapt to multi-scale changes of sheep. In the sheep density data set, the method obtained the lowest mean absolute error (MAE) and root mean square error (RMSE). In addition, a convolutional neural network model based on view branch sharing is also studied. Compared with the five popular methods, this method can achieve better performance. It is applied to solve the problem of pedestrian scale change and chaotic distribution in complex scenes; The performance of this method is better than that of comparison method, and the application results in actual scenarios verify the effectiveness of this method.