湖北民族大学学报（自然科学版）2025，Vol.43Issue(3)：334-340,7.DOI:10.13501/j.cnki.42-1908/n.2025.09.003
基于改进多尺度特征金字塔网络的轻量化图像超分辨率重建模型
Lightweight Image Super-resolution Reconstruction Model Based on Improved Multi-scale Feature Pyramid Network
摘要
Abstract
To address the challenges of excessive parameter size and training difficulties caused by complex neural network architectures in image super-resolution(SR)tasks,a lightweight image SR reconstruction model based on improved multi-scale feature pyramid network(MFPNet)was proposed.First,an improved MFPNet architecture was designed.The feature representation spaces at different scales were constructed through iterative downsampling operations,which effectively enhanced the network's ability to capture multi-granularity detail features of images.Second,position aware circular convolution(ParC)was adopted as the primary feature extraction module,reducing the parameter count while expanding the network's receptive field size.Finally,a dynamic attention block(DAB)was developed.Through an attention guidance layer(AGL),the weighting of efficient channel attention(ECA)and spatial attention(SA)modules was dynamically adjusted,improving the network's capability for restoring texture details.The experimental results demonstrated that,compared with other state-of-the-art models,the structural similarity index measure(SSIM)maximum of 0.9613 and the peak signal-to-noise ratio(PSNR)maximum of 38.11 dB were achieved by improved MFPNet model.This research confirmed that the improved MFPNet model could be used to image reconstruction tasks with more natural detail textures.关键词
卷积神经网络/超分辨率重建/注意力机制/轻量化/多尺度特征金字塔Key words
convolutional neural network/super-resolution reconstruction/attention mechanism/lightweight/multi-scale feature pyramid分类
信息技术与安全科学引用本文复制引用
许光宇,吴敏..基于改进多尺度特征金字塔网络的轻量化图像超分辨率重建模型[J].湖北民族大学学报（自然科学版）,2025,43(3):334-340,7.基金项目
国家自然科学基金项目(61471004) （61471004）
安徽理工大学博士专项基金项目(ZX942). （ZX942）
