At present， the image super-resolution networks based on deep learning are mainly implemented by convolution. Compared with the traditional Convolutional Neural Network （CNN）， the main advantage of Transformer in the image super-resolution task is its long-distance dependency modeling ability. However， most Transformer-based image super-resolution models cannot establish global dependencies with small parameters and few network layers， which limits the performance of the model. In order to establish global dependencies in super-resolution network， an image Super-Resolution network based on Global Dependency Transformer （GDTSR） was proposed. Its main component was the Residual Square Axial Window Block （RSAWB）， and in Transformer residual layer， axial window and self-attention were used to make each pixel globally dependent on the entire feature map. In addition， the super-resolution image reconstruction modules of most current image super-resolution models are composed of convolutions. In order to dynamically integrate the extracted feature information， Transformer and convolution were combined to jointly reconstruct super-resolution images. Experimental results show that the Peak Signal-to-Noise Ratio （PSNR） and Structural Similarity Index （SSIM） of GDTSR on five standard test sets， including Set5， Set14， B100， Urban100 and Manga109， are optimal for three multiples （ \times \mathrm{2} ， \times \mathrm{3} ， \times \mathrm{4} ）， and on large-scale datasets Urban100 and Manga109， the performance improvement is especially obvious.

By comprehensively comparing the models of traditional knowledge graph representation learning， including the advantages and disadvantages and the applicable tasks， the analysis shows that the traditional single-modal knowledge graph cannot represent knowledge well. Therefore， how to use multimodal data such as text， image， video， and audio for knowledge graph representation learning has become an important research direction. At the same time， the commonly used multimodal knowledge graph datasets were analyzed in detail to provide data support for relevant researchers. On this basis， the knowledge graph representation learning models under multimodal fusion of text， image， video， and audio were further discussed， and various models were summarized and compared. Finally， the effect of multimodal knowledge graph representation on enhancing classical applications， including knowledge graph completion， question answering system， multimodal generation and recommendation system in practical applications was summarized， and the future research work was prospected.

When using the slicing method to measure the point cloud volumes of irregular objects， the existing Polygon Splitting and Recombination （PSR） algorithm cannot split the nearer contours correctly， resulting in low calculation precision. Aiming at this problem， a multi-contour segmentation algorithm — Improved Nearest Point Search （INPS） algorithm was proposed. Firstly， the segmentation of multiple contours was performed through the single-use principle of local points. Then， Point Inclusion in Polygon （PIP） algorithm was adopted to judge the inclusion relationship of contours， thereby determining positive or negative property of the contour area. Finally， the slice area was multiplied by the thickness and the results were accumulated to obtain the volume of irregular object point cloud. Experimental results show that on two public point cloud datasets and one point cloud dataset of chemical electron density isosurface， the proposed algorithm can achieve high-accuracy boundary segmentation and has certain universality. The average relative error of volume measurement of the proposed algorithm is 0.043 6%， which is lower than 0.062 7% of PSR algorithm， verifying that the proposed algorithm achieves high accuracy boundary segmentation.

Concerning the key problems of selecting threshold function in wavelet threshold denoising, in order to address the discontinuity of conventional threshold function and large deviation existing in the estimated wavelet coefficients, a continuous adaptive threshold function in the whole wavelet domain was proposed. It fully considered the characteristics of different sub-band coefficients in different scales, and set the energy of sub-band coefficients in different scales as threshold function's initial weights. Optimal weights were iteratively solved by using interval advanced-retreat method and golden section method, so as to adaptively improve approximation level between estimated and decomposed wavelet coefficients. The experimental results show that the proposed method can both efficiently reduce noise and simultaneously preserve the edges and details of image, also achieve higher Peak Signal-to-Noise Ratio (PSNR) under different noise standard deviations.

A new fusion method of multi-focus images based on the four-channel non-separable wavelet was proposed, which aimed to solve the problem which exists in the separable wavelet-based fusion methods. First, a 4×4 non-separable wavelet 4-channel filter bank with linear phase using the theory of non-separable wavelets was constructed. Then images involving the fusion were decomposed by using the filter bank, for low-frequency part, the average value was selected, for the three high-frequency parts of each level, the value of the area window whose energy was bigger was selected. Finally, the new fused image was reconstructed. The performance of the method was evaluated using entropy, average gradient, etc. The experimental results show that it has good effect on the fusion of multi-focus images. The performance is better than that of the separable wavelet fusion method by using the same fusion algorithm. According to this method, the fused images are clearer and the detailed edge information of low-frequency domain is better obtained.