Computer Science ›› 2026, Vol. 53 ›› Issue (5): 237-246.doi: 10.11896/jsjkx.250400097

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Continuous Image Super-resolution Based on Self-attention Implicit Feature Encoding andDecoding

CHEN Boying, SHI Jie   

  1. School of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu 215000, China
  • Received:2025-04-21 Revised:2025-07-02 Published:2026-05-08
  • About author:CHEN Boying,born in 1999,postgra-duate.His main research interests include deep learning and image super-resolution algorithm.
    SHI Jie,born in 1978,Ph.D,professor.His main research interests include deep learning,artificial intelligence,intelligent perception of power equipment.
  • Supported by:
    General Projects of the National Natural Science Foundation of China(52477114).

Abstract: In order to solve the problem that the convolutional neural network can only deal with fixed resolution in image super-resolution reconstruction,and it is difficult to realize continuous super-resolution reconstruction with arbitrary resolution,this paper proposes an algorithm for super-resolution reconstruction of continuous-rate images based on self-attention implicit feature encoding and decoding.Firstly,the feature encoding network based on local-global self-attention modeling is used to realize the feature mapping from low-resolution image to high-dimensional feature.Then local implicit feature enhancement encoders are used to effectively aggregate local neighborhood features,and self-attention mechanism is used to enhance the correlation between feature neighborhood data.Finally a multi-granularity implicit feature decoder is used to predict the pixel values of high-resolution coordinates by inputting the image coordinates and the depth features of the adjacent coordinate multilayer perceptron.Experimental results show that,compared with the current image reconstruction algorithm,the proposed method achieves better super-resolution reconstruction results,which proves the superiority and effectiveness of the method.

Key words: Continuous super-resolution reconstruction, Neural implicit representation, Local implicit features, Attention mechanism

CLC Number: 

  • TP391
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