Research on image super-resolution based on attention mechanism and multi-scale

Abstract

In order to solve the problem of the single feature scale of the generated image in the SISR field and the lack of texture information, a parallel generation confrontation network structure based on the attention mechanism and multi-scale is proposed on the basis of SRGAN, which adopts a dual generator and discriminator combined with attention module model. Train the network to learn multi-scale features, and integrate high-frequency information of different scales in the residual network. The experimental results on Set5, Set14, and BSD100 benchmark data sets prove that the algorithm has a good effect in restoring image detail information.

Implementation of Network Data Mining Algorithm for Associated Users Based on Multi-Information Fusion

“…Du and Zhao proposed CTR (collaborative topic regression) model. CTR realizes the effective combination of implicit Dirichlet allocation (LDA) and probability matrix decomposition PMF in a tightly coupled manner [7].Ren and Li used the deep convolution neural network CNN to extract the features of pictures, combined the interactive information between users and pictures at the last full connection layer, and then output the sorting of labels [8]. Huang et al first used the recurrent neural network (RNN) to model the user's historical click records and then used the feedback neural network (FNN) to simulate multi-information fusion and finally produce the recommendation results [9].Ji et al proposed an algorithm that can combine structured data and unstructured data for recommendation.…”