采用感受野优化与渐进特征融合的图像超分辨率算法

吴洪伍; 盖绍彦; 达飞鹏

doi:10.7652/xjtuxb202501013

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采用感受野优化与渐进特征融合的图像超分辨率算法

更新时间：2026-01-06

- 采用感受野优化与渐进特征融合的图像超分辨率算法
- Image Super Resolution Algorithm Based on Receptive Field Optimization and Progressive Feature Fusion
- 西安交通大学学报 2025年59卷第1期页码：136-147
- 作者机构：
  
  1.东南大学自动化学院， 210096，南京
  2.东南大学复杂工程系统测量与控制教育部重点实验室， 210096，南京
- 作者简介：
  
  吴洪伍（1998—），男，硕士生；盖绍彦（1979—），男，副教授，博士生导师。
- 基金信息：
  
  江苏省前沿引领技术基础研究专项资助项目(BK20192004C)
- DOI：10.7652/xjtuxb202501013
  中图分类号： TP391
- 收稿：2024-04-07，
  
  网络首发：2024-07-18，
  
  纸质出版：2025-01-10
- 稿件说明：
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吴洪伍, 盖绍彦, 达飞鹏. 采用感受野优化与渐进特征融合的图像超分辨率算法[J]. 西安交通大学学报, 2025,59(1):136-147.

WU Hongwu, GAI Shaoyan, DA Feipeng. Image Super Resolution Algorithm Based on Receptive Field Optimization and Progressive Feature Fusion[J]. Journal of Xi’an Jiaotong University, 2025, 59(1): 136-147.
吴洪伍, 盖绍彦, 达飞鹏. 采用感受野优化与渐进特征融合的图像超分辨率算法[J]. 西安交通大学学报, 2025,59(1):136-147. DOI： 10.7652/xjtuxb202501013.

WU Hongwu, GAI Shaoyan, DA Feipeng. Image Super Resolution Algorithm Based on Receptive Field Optimization and Progressive Feature Fusion[J]. Journal of Xi’an Jiaotong University, 2025, 59(1): 136-147. DOI： 10.7652/xjtuxb202501013.

摘要

针对现有基于深度学习的超分辨率方法存在参数冗余以及难以学习到全局上下文信息、重建图像高频特征能力欠佳的问题，提出一种基于感受野优化与渐进特征融合的超分辨率网络（RPSRnet），其在单幅图像重建方面实现了较高的性能。该网络采用像素注意力机制与大感受野卷积相结合的方式，设计两条渐进路径将输入表征为不同层次的特征抽象，增强网络捕获上下文信息的能力，同时减少了网络参数冗余。通过分层卷积和多重感受野分支，在保持轻量卷积的前提下，于分层的多路径上学习不同尺度的融合特征，增强网络重建边缘细节和复杂纹理特征的能力。实验结果表明：相比于先进算法，所提算法在基准测试集Set5上的峰值信噪比达到32.47 dB，在测试集Set14上达到28.81 dB，优于现有的先进算法，且网络参数更少，实现了9%的参数缩减，从而验证了算法的有效性。

Abstract

To tackle the issue of parameter redundancy and the challenge of learning comprehensive global contextual information in current deep learning-based super-resolution methods

as well as the limited capacity to reconstruct high-frequency image features

a super-resolution network (RPSRnet) based on receptive field optimization and progressive feature fusion is proposed

delivering exceptional performance in single image reconstruction. By integrating pixel attention mechanisms and large receptive field convolutions

two progressive pathways are devised to interpret inputs as features at varying levels of abstraction. This design enriches the network's capability to grasp contextual information while streamlining parameter redundancy. Through the utilization of hierarchical convolutions and multiple receptive field branches

the network sustains lightweight convolutions and acquires fused features from diverse scales on hierarchical pathways. This process enhances the network's proficiency in reconstructing edge details and intricate texture features. The experimental results reveal that the proposed algorithm achieves a peak signal-to-noise ratio of 32.47 dB on the Set5 benchmark test set and 28.81 dB on the Set14 test set. The algorithm surpasses existing advanced algorithms

utilizing fewer parameters and resulting in a 9% reduction in parameters

effectively validating the algorithm's efficacy.

关键词

Keywords

references

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