Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.

This article proposes a model for Chinese text classification based on a feature-enhanced nonequilibrium bidirectional long short-term memory (Bi-LSTM) network that analyzes Chinese text information in depth and improves the accuracy of text classification. First, the bidirectional encoder representations from transformers model was used to vectorize the original Chinese corpus and extract preliminary semantic features. Then, a nonequilibrium Bi-LSTM network was applied to increase the weight of text information containing important semantics and further improve the effects of the key features in Chinese text classification. Simultaneously, a hierarchical attention mechanism was used to widen the gap between the important and unimportant data. Finally, the softmax function was used for classification. By comparing the classification performance of the proposed scheme with those of various other models, it was observed that the model substantially improved the precision of Chinese text classification and had a strong ability to recognize Chinese text features. The model achieved 97% precision on the experimental dataset.