轻量级注意力特征选择循环网络的超分重建  

Lightweight attention feature selection recursive network for super-resolution

作　　者：徐雯捷 宋慧慧 袁晓彤[1,2] 刘青山 Xu Wenjie;Song Huihui;Yuan Xiaotong;Liu Qingshan(Collaborative Innovation Center on Atmospheric Environment and Equipment Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Key Laboratory of Big Data Analysis Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学大气环境与装备技术协同创新中心,南京210044 [2]江苏省大数据分析技术重点实验室,南京210044

出　　处：《中国图象图形学报》2021年第12期2826-2835,共10页Journal of Image and Graphics

基　　金：国家新一代人工智能重大项目(2018AAA0100400);国家自然科学基金项目(61872189,61532009);江苏省自然科学基金项目(BK20191397);江苏省研究生科研与实践创新计划项目(KYCX20_0968)。

摘　　要：目的深度卷积网络在图像超分辨率重建领域具有优异性能,越来越多的方法趋向于更深、更宽的网络设计。然而,复杂的网络结构对计算资源的要求也越来越高。随着智能边缘设备(如智能手机)的流行,高效能的超分重建算法有着巨大的实际应用场景。因此,本文提出一种极轻量的高效超分网络,通过循环特征选择单元和参数共享机制,不仅大幅降低了参数量和浮点运算次数(floating point operations,FLOPs),而且具有优异的重建性能。方法本文网络由浅层特征提取、深层特征提取和上采样重建3部分构成。浅层特征提取模块包含一个卷积层,产生的特征循环经过一个带有高效通道注意力模块的特征选择单元进行非线性映射提取出深层特征。该特征选择单元含有多个卷积层的特征增强模块,通过保留每个卷积层的部分特征并在模块末端融合增强层次信息。通过高效通道注意力模块重新调整各通道的特征。借助循环机制(循环6次)可以有效提升性能且大幅减少参数量。上采样重建通过参数共享的上采样模块同时将浅层与深层特征进放大、融合得到高分辨率图像。结果与先进的轻量级网络进行对比,本文网络极大减少了参数量和FLOPs,在Set5、Set14、B100、Urban100和Manga109等基准测试数据集上进行定量评估,在图像质量指标峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity,SSIM)上也获得了更好的结果。结论本文通过循环的特征选择单元有效挖掘出图像的高频信息,并通过参数共享机制极大减少了参数量,实现了轻量的高质量超分重建。Objective Deep convolutional neural network has shown strong reconstruction ability in image super-resolution(SR)task.Efficient super-resolution has a great practical application scenario due to the popularity of intelligent edge devices such as mobile phones.A very lightweight and efficient super-resolution network has been proposed.The proposed method has reduced the number of parameters and floating point operations(FLOPs)greatly and achieved excellent reconstruction performance based on recursive feature selection module and parameter sharing mechanism.Method The proposed lightweight attention feature selection recursive network(AFSNet)has mainly evolved three key components:low-level feature extraction,high-level feature extraction and upsample reconstruction.In the low-level feature extraction part,the input low-resolution image has passed through a 3×3 convolutional layer to extract the low-level features.In the high-level feature extraction part,a recursive feature selection module(FSM)to capture the high-level features has been designed.At the end of the network,a shared upsample block to super-resolve low-level and high-level features has been utilized to obtain the final high-resolution image.Specifically,the FSM has contained a feature enhancement block and an efficient channel attention block.The feature enhancement block has four convolutional layers.Different from other cascaded convolutional layers,this block has retained part of features in each convolutional layer and fused them at the end of this module.Features extracted from different convolutional layers have different levels of hierarchical information,so the proposed network can choose to preserve part of them step-by-step and aggregate them at the end of this module.An efficient channel attention(ECA)block has been presented following the feature enhancement block.Different from the channel attention(CA)in the residual channel attention networks(RCAN),the ECA has avoided the dimensionality reduction operation,which involves two 1×1 conv

关 键 词：图像超分辨率 轻量网络 递归机制 参数共享 特征增强 高效通道注意力 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]