ABSTRACT Adding Semantics to RosettaNet Specifications

人工智能是一门新兴的具有挑战力的学科。

自人工智能诞生以来,发展迅速,产生了许多分支。

诸如强化学习、模拟环境、智能硬件、机器学习等。

但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。

下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。

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名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱（共包含二级节点15 个，三级节点93 个）间序列分析)监督学习)领域 二级分类 三级分类。

改进Retinex⁃Net的低光照图像增强算法欧嘉敏1 胡　晓1 杨佳信1摘　要　针对Retinex⁃Net存在噪声较大㊁颜色失真的问题,基于Retinex⁃Net的分解-增强架构,文中提出改进Ret⁃inex⁃Net的低光照图像增强算法.首先,设计由浅层上下采样结构组成的分解网络,将输入图像分解为反射分量与光照分量,在此过程加入去噪损失,抑制分解过程产生的噪声.然后,在增强网络中引入注意力机制模块和颜色损失,旨在增强光照分量亮度的同时减少颜色失真.最后,反射分量和增强后的光照分量融合成正常光照图像输出.实验表明,文中算法在有效提升图像亮度的同时降低增强图像噪声.关键词　低光照图像增强,深度网络,视网膜大脑皮层网络(Retinex⁃Net),浅层上下采样结构,注意机制模块引用格式　欧嘉敏,胡晓,杨佳信.改进Retinex⁃Net的低光照图像增强算法.模式识别与人工智能,2021,34(1):77-86.DOI　10.16451/ki.issn1003⁃6059.202101008 中图法分类号　TP391.4Low⁃Light Image Enhancement Algorithm Based onImproved Retinex⁃NetOU Jiamin1,HU Xiao1,YANG Jiaxin1ABSTRACT　Aiming at the problems of high noise and color distortion in Retinex⁃Net algorithm,a low⁃light image enhancement algorithm based on improved Retinex⁃Net is proposed grounded on the decomposition⁃enhancement framework of Retinex⁃Net.Firstly,a decomposition network composed of shallow upper and lower sampling structure is designed to decompose the input image into reflection component and illumination component.In this process,the denoising loss is added to suppress the noise generated during the decomposition process.Secondly,the attention mechanism module and color loss are introduced into the enhancement network to enhance the brightness of the illumination component and meanwhile reduce the image color distortion.Finally,the reflection component and the enhanced illumination component are fused into the normal illumination image to output.The experimental results show that the proposed algorithm improves the image brightness effectively with the noise of enhanced image reduced.Key Words　Low⁃Light Image Enhancement,Deep Network,Retinal Cortex Theory⁃Net,Shallow Up⁃per and Lower Sampling Structure,Attention Mechanism ModuleCitation　OU J M,HU X,YANG J X.Low⁃Light Image Enhancement Algorithm Based on Improved Retinex⁃Net.Pattern Recognition and Artificial Intelligence,2021,34(1):77-86.收稿日期:2020-05-12;录用日期:2020-09-17 Manuscript received May12,2020;accepted September17,2020国家自然科学基金项目(No.62076075)资助Supported by National Natural Science Foundation of China(No. 62076075)本文责任编委黄华Recommended by Associate Editor HUANG Hua1.广州大学电子与通信工程学院　广州5100061.School of Electronics and Communication Engineering,Guang⁃zhou University,Guangzhou510006 由于低光照环境和有限的摄像设备,图像存在亮度较低㊁对比度较低㊁噪声较大㊁颜色失真等问题,不仅会影响图像的美学㊁人类的视觉感受,还会降低运用正常光照图像的高级视觉任务的性能[1-3].为了有效改善低光图像质量,学者们提出许多低光照图像增强算法,经历灰度变换[4-5]㊁视网膜皮层理论[6-11]和深度神经网络[12-19]三个阶段.早期,通过直方图均衡[4-5]㊁伽马校正等灰度变换方法对低亮度区域进行灰度拉抻,可达到提高暗区亮度的目的.然而,因为未考虑像素与其邻域像素的关系,灰度变换常会导致增强图像缺乏真实感.第34卷　第1期模式识别与人工智能Vol.34　No.1 2021年1月Pattern Recognition and Artificial Intelligence Jan.　2021Land [6]提出视网膜皮层理论(Retinal CortexTheory,Retinex).该理论认为物体颜色与光照强度无关,即物体具有颜色恒常性.基于该理论,相继出现经典的单尺度视网膜增强算法(Single Scale Retinex,SSR)[7]和色彩恢复的多尺度视网膜增强算法(Multi⁃scale Retinex with Color Restoration,MSR⁃CR)[8].算法主要思想是利用高斯滤波器获取低光照图像的光照分量,再通过像素间逐点操作求得反射分量作为增强结果.Wang 等[9]利用亮通滤波器和对数变换平衡图像亮度和自然性,使增强后的图像趋于自然.Fu 等[10]设计用于同时估计反射和光照分量(Simultaneous Reflectance and Illumination Esti⁃mation,SRIE)的加权变分模型,可有效处理暗区过度增强的问题.Guo 等[11]提出仅估计光照分量的低光照图像增强算法(Low Light Image Enhancementvia Illumination Map Estimation,LIME),主要利用局部一致性和结构感知约束条件计算图像的反射分量并作为输出结果.然而,这些基于Retinex 理论模型的算法虽然可调整低光图像亮度,但增亮程度有限.研究者发现卷积神经网络(Convolutional NeuralNetwork,CNN)[12]与Retinex 理论结合能进一步提高增强图像的视觉效果,自动学习图像的特征,解决Retinex 依赖手工设置参数的问题.Lore 等[13]提出深度自编码自然低光图像增强算法(A Deep Auto⁃encoder Approach to Natural Low Light Image Enhan⁃cement,LLNet),有效完成低光增强任务.Lü等[14]提出多边低光增强网络(Multi⁃branch Low⁃Light En⁃hancement Network,MBLLEN),学习低光图像到正常光照图像的映射.Zhang 等[15]结合最大信息熵和Retinex 理论,提出自监督的光照增强网络.Wei 等[16]基于图像分解思想,设计视网膜大脑皮层网络(Retinex⁃Net),利用分解-增强架构调整图像亮度.Zhang 等[17]基于Retinex⁃Net 设计低光增强器.然而,由于噪声与光照水平有关,Retinex⁃Net 提取反射分量后,图像暗区噪声高于亮区.因此,Retinex⁃Net 的增强结果存在噪声较大㊁颜色失真的问题,不利于图像质量的提升.为此本文提出改进Retinex⁃Net 的低光照图像增强算法.以Retinex⁃Net 的分解与增强框架为基础,针对噪声问题,在分解网络采用浅层上下采样结构[15],利用反射分量梯度项[15]作为损失.同时为了改善增强图像的色彩偏差,保留丰富的细节信息,在增强网络中嵌入注意力机制模块[18]和颜色损失[19].实验表明,本文算法在LOL 数据集和其它公开数据集上取得较优的视觉效果和客观结果.1　改进Retinex⁃Net 的低光照图像增强算法改进Retinex⁃Net 的低光照图像增强算法框图如图1所示.S lowS normalR highI highI lowR lowI end图1　本文算法框图Fig.1　Flowchart of the proposed algorithm87模式识别与人工智能(PR&AI) 第34卷 Retinex理论[6]认为彩色图像可分解为反射分量和光照分量:S=R I.(1)其中: 表示逐像素相乘操作;S表示彩色图像,可以是任何具有不同曝光程度的图像;R表示反射分量,反映物体内在固有属性,与外界光照无关;I表示光照分量,不同曝光度的物体光照分量不同.本文算法主要利用2个相互独立训练的子网络,分别是分解网络与增强网络.具体地说,首先,分解网络以数据驱动方式学习,将低光照图像和与之配对的正常光照图像分解为相应的反射分量(R low, R normal)和光照分量(I low,I normal).然后,增强网络以低光图像的光照分量I low作为输入,在结构感知约束下,提升光照分量的亮度.最后,重新组合增强的光照分量I en与反射分量R low,形成增强图像S en,作为网络输出.1.1　分解网络的浅层上下采样结构由于式(1)是一个不适定问题[20],很难设计适用于多场景的约束函数.本文算法以数据驱动的方式进行学习,不仅能解决该问题,还能进一步提高网络的泛化能力.如图1所示,在训练阶段,分解网络以低光照图像S low和与之对应的正常光照图像S normal 作为输入,在约束条件下学习输出它们一致的反射分量R low和R normal,及不同的光照分量I low和I normal.值得注意的是,S low与S normal共享分解网络的参数.区别于常用的深度U型网络(U⁃Net)结构及Retinex⁃Net简单的堆叠卷积层,本文算法的分解网络是一个浅层的上下采样结构,由卷积层与通道级联操作组成,采样层只有4层,网络训练更简单.实验表明,运用此上下采样结构变换图像尺度时,下采样操作一定程度上舍去含有噪声的像素点,达到降噪效果的目的,但同时会引起图像的模糊.因此为了提高分解图像清晰度,减少语义特征丢失,在图像上采样后应用通道数级联操作,可给图像补偿下采样丢失的细节信息,增强清晰度.在浅层上下采样结构中,首先,使用1个9×9的卷积层提取输入图像S low的特征.然后,采用5层以ReLU作为激活函数的卷积层变换图像尺度,学习反射分量与光照分量的特征.最后,分别利用2层卷积层及Sigmoid函数,将学习到的特征映射成反射图R low和光照图I low后再输出.对于分解网络的约束损失,本文算法沿用Retinex⁃Net的重构损失l rcon㊁不变反射率损失l R及光照平滑损失l I.另外为了在分解网络中更进一步减小噪声,添加去噪损失l d.因此总损失如下:l=l rcon+λ1l R+λ2l I+λ3l d,其中,λ1㊁λ2㊁λ3为权重系数,用于平衡各损失分量.对于L1㊁L2范数和结构相似性(Structural Similarity, SSIM)损失的选择,当涉及图像质量任务时,L2范数与人类视觉对图像质量的感知没有很好的相关性,在训练中容易陷入局部最小值,而SSIM虽然能较好地学习图像结构特征,但对平滑区域的误差敏感度较低,引起颜色偏差[21].因此本文算法使用L1范数约束所有损失.在分解网络中输出的结果R low和R normal都可与光照图重构成新的图像,则重构损失如下:l rcon=∑i=low,normalW1Rlow I i-S i1+∑j=low,normal W2R normal I j-S j1,其中 表示逐像素相乘操作.当i为low或j为normal 时,权重系数W1=W2=1,否则W1=W2=0.001.对于配对的图像,使用较大的权重能够使分解网络更好地学习配对图像的特征.对于配对的图像对,使用较大的权重可使分解网络更好地学习配对图像的特征.不变反射率损失l R是基于Retinex理论的颜色恒常性,在分解网络中主要用于约束学习不同光照图像的一致反射率:l R=Rlow-R normal1.对于光照平滑损失l I,本文采用结构感知平滑损失[16].该损失以反射分量梯度项作为权重,在图像梯度变化较大的区域,光照变得不连续,从而亮度平滑的光照图能保留图像结构信息,则l I=ΔIlow exp(-λgΔR low)1+ΔI normal exp(-λgΔR normal)1,其中,Δ表示图像水平和垂直梯度和,λg表示平衡系数.Rudin等[22]观察到,噪声图像的总变分(Total Variation,TV)大于无噪图像,通过限制TV可降低图像噪声.然而在图像增强中,限制TV相当于最小化梯度项.受TV最小化理论[22-23]启发,本文引入反射分量的梯度项作为损失,用于控制反射图像噪声,故称为去噪损失:l d=λΔRlow1.当λ值增加时,噪声减小,同时图像会模糊.因此对于权重参数的选择十分重要,经过实验研究发现,当权重λ=0.001时,图像获得较好的视觉效果.1.2　增强网络的注意力机制如图1所示,增强网络以分解网络的输出I low作97第1期 欧嘉敏　等:改进Retinex⁃Net的低光照图像增强算法为输入,学习增强I low的亮度,将增强结果I en与分解网络另一输出R low重新结合为增强图像S en后输出.在增强网络中,I low经过多个下采样块生成较小尺度图像,使增强网络有较大尺度角度分配光照,从而具有调节亮度的能力.网络采用上采样方式重构局部光照,对亮的区域分配较低亮度,对较暗的区域调整较高亮度.此外,将上采样层的输出进行通道数的级联,在调整不同局部光照的同时,保持全局光照一致性.而且跳过连接是从下采样块引入相应的上采样块,通过元素求和,强制网络学习残差.针对Retinex⁃Net出现的颜色失真问题,在增强网络中嵌入注意力机制模块.值得注意的是,与其它复杂的注意力模块不同,注意力机制模块由简单卷积层和激活操作组成,不要求强大的硬件设备,也不需要训练多个模型和大量额外参数.在光照调整过程中,可减少对无关背景的特征响应,只激活感兴趣的特征,提高算法对图像细节的处理能力和对像素的敏感性,指导网络既调整图像亮度又保留图像结构.由图1可见,注意力模块的输入是图像特征αi㊁βi,输出为图像特征γi,i=1,2,3,表示注意力机制模块的序号.αi为下采样层输出的图像特征,βi为上采样层的输出特征.这2个图像特征分别携带不同的亮度信息,两者经过注意力模块后,降低亮度无关特征(如噪声)的响应,使输出特征γi携带更多亮度信息被输入到下一上采样层,提高网络对亮度特征的学习能力.αi与重建尺度后的βi分别经过一个独立的1×1卷积层,在ReLU激活之前进行加性操作.依次经过1×1卷积层㊁Sigmoid函数,最后与βi通过逐元素相乘后将结果与αi进行通道级联.在此传播过程中,注意机制可融合不同尺度图像信息,同时减少无关特征的响应,增强网络调整亮度能力.独立于分解网络的约束损失,增强网络调整光照程度是基于局部一致性和结构感知[16]的假设.本文算法除了沿用Retinex⁃Net中约束增强网络的损失外,在实验中,针对Retinex⁃Net出现的色彩偏差,增加颜色损失[19],因此增强网络损失:L=L rcon+L I+μL c,其中,L rcon为增强图像的重构损失,L rcon=Snormal-R low I en1,L I表示结构感知平滑损失,L c表示本文的颜色损失,μ表示平衡系数.L rcon定义表示增强后的图像与其对应的正常光照图像的距离项,结构感知平滑损失L I 与分解网络的平滑损失类似,不同的是,在增强网络中,I en以R low的梯度作为权重系数:L I=ΔIen exp(-λgΔR low)1.此外,本文添加颜色损失L c,衡量增强图像与正常光照图像的颜色差异.先对2幅图像采用高斯模糊,滤除图像的纹理㊁结构等高频信息,留下颜色㊁亮度等低频部分.再计算模糊后图像的均方误差.模糊操作可使网络在限制纹理细节干扰情况下,更准确地衡量图像颜色差异,进一步学习颜色补偿.颜色损失为L c=F(Sen)-F(S normal)21.其中:F(x)表示高斯模糊操作,x表示待模糊的图像.该操作可理解为图像每个像素以正态分布权重取邻域像素的平均值,从而达到模糊的效果,S en为增强图像,S normal为对应的正常光照图像,F(x(i,j))=∑k,lx(i+k,j+l)G(k,l),G(k,l)表示服从正态分布的权重系数.在卷积网络中G(k,l)相当于固定大小的卷积核,G(k,l)=0.æèçç053exp k2-l2öø÷÷6.2　实验及结果分析2.1　实验环境本文算法采用LOL训练集[16]和合成数据集[16]训练网络.测试集选取LOL的评估集㊁DICM数据集㊁MEF数据集.在训练过程中,网络采用图像对训练,批量化大小(Batch Size)设为32,块大小(Patch Size)设为48×48.分解网络的损失平衡系数λ1=0.001,λ2=0.1,λ3=0.001.增强网络的平衡系数μ=0.01,λg=10.本文采用自适应矩估计优化器(Adaptive Moment Estima⁃tion,Adam).网络的训练和测试实验均在Nvidia GTX2080GPU设备上完成,实现代码基于TensorFlow框架.为了验证本文算法的性能及效果,采用如下对比算法:Retinex⁃Net,SRIE[10],LIME[11],MBLLEN[14]㊁文献[15]算法㊁全局光照感知和细节保持网络(Global Illumination⁃Aware and Detail⁃Preserving Net⁃work,GLADNet)[24]㊁无成对监督深度亮度增强(Deep Light Enhancement without Paired Supervision, EnlightenGAN)[25].在实验过程中,均采用原文献提供的模型或源代码对图像进行测试.采用如下客观评估指标:峰值信噪比(Peak Signal to Noise Ratio,PSNR)㊁结构相似性(Structural08模式识别与人工智能(PR&AI) 第34卷Similarity,SSIM)[26]㊁自然图像质量评估(NaturalQuality Evaluator,NIQE)[27]㊁通用图像质量评估(Universal Quality Index,UQI)[28]㊁基于感知的图像质量评估(Perception⁃Based Image Quality Evaluator,PIQE)[29].SSIM㊁PSNR㊁UQI 值越高,表示增强结果图质量越优.相反,PIQE㊁NIQE 值越高,表示图像质量越差.2.2　消融性实验为了进一步验证本文算法各模块的有效性,以Retinex⁃Net 为基础设计消融性实验,利用PSNR 衡量噪声水平,采用SSIM 从亮度㊁对比度㊁结构评估图像综合质量.实验结果如表1所示,表中S⁃ULS 表示浅层上下采样结构,l d 表示去噪损失.Enhan _I low 表示增强网络输入仅为光照分量,AMM 表示注意力机制模块,L c 表示颜色损失.参数微调1表示增强网络的平滑损失系数由原Retinex⁃Net 的3设为1;参数微调2是增强网络的平滑损失系数为1,批量化大小由16设为32.表1　各改进模块及损失的消融性实验结果Table 1　Ablation experiment results of improved modules and loss序号基础框架改进方法PSNRSSIM1-Retinex⁃Net 16.7740.55923Retinex⁃Net 添加S⁃ULS,不添加l d 添加S⁃ULS,添加l d17.45217.4940.6890.699456Retinex⁃Net+S⁃ULS+l d添加Enhan _I low ,不添加AMM,不添加L c 添加Enhan _I low ,添加AMM,不添加L c 添加Enhan _I low ,添加AMM,添加L c17.89718.00218.0910.7030.7080.70478Retinex⁃Net+S⁃ULS+l d +AMM+L c参数微调1参数微调218.27218.5290.7190.720 表1中序号2给出以Retinex⁃Net 为基础,采用浅层上下采样结构作为分解网络的结果.相比Retinex⁃Net,PSNR 值显著提高,表明此结构可抑制由图像分解带来的噪声.在此基础上添加去噪损失,进一步降低噪声,见序号3.由此验证浅层上下采样结构与去噪损失的有效性.在本文算法中,由于采用两步训练的方式,即先训练分解网络后训练增强网络,因此在验证浅层上下采样结构和去噪损失的有效性后,以此为基础评估增强网络引入的注意力机制模块和颜色损失的有效性.在Retinex⁃Net 中增强网络的输入为反射分量与光照分量通道级联后的结果.该设置一定程度上会导致反射分量丢失图像结构和细节,同时影响光照分量的亮度提升.为此,先设置序号4的实验验证上述分析.由结果可见:PSNR㊁SSIM 值大幅提高,证明此分析的正确性,表明本文算法的增强网络仅以光照分量作为输入的有效性.另外,从序号5结果看出,利用注意力模块后,图像噪声显著降低,这归功于注意力模块可减少对图像无关特征的响应,集中注意力学习亮度特征,从而降低图像噪声水平.在颜色损失的消融性实验中,尽管客观数值上没有直观体现颜色的恢复,但根据图2和图3可知,该损失是有效的.为了使各模块更好地发挥优势,本文算法对参数进行微调.从序号7㊁序号8的实验结果可见,微调参数后本文算法各模块作用进一步体现,取得更优结果. (a)输入图像 (b)参考图像 (a)Input image (b)Ground truth18第1期 欧嘉敏　等:改进Retinex⁃Net 的低光照图像增强算法 (c)SRIE (d)LIME (e)GLADNet (f)MBLLEN (g)EnlightenGAN (h)Retinex⁃Net (i)文献[15]算法 (j)本文算法 (i)Algorithm in reference[15]　(j)The proposed algorithm图2　各算法在LOL数据集上的视觉效果Fig.2　Visual results of different algorithms on LOLdatasetA B C D(a)输入图像(a)Inputimages(b)LIME(c)GLADNet28模式识别与人工智能(PR&AI) 第34卷(d)MBLLEN(e)EnlightenGAN(f)Retinex⁃Net(g)文献[15]算法(g)Algorithm in reference[15](h)本文算法(h)The proposed algorithm图3　各算法在DICM㊁MEF数据集上的视觉效果Fig.3　Visual results of different algorithms on DICM and MEF datasets38第1期 欧嘉敏　等:改进Retinex⁃Net的低光照图像增强算法2.3　对比实验各算法在3个数据集上的客观评估结果如表2所示,表中黑体数字表示最优结果,斜体数字表示次优结果.在LOL 数据集上,SSIM 可从亮度㊁对比度㊁结构度量2幅图像相似度,与人类视觉系统(Human Vision System,HVS)具有较高相关性[21,30],可较全面体现图像质量.从表2可见,在SSIM㊁UQI 指标上,本文算法取得最高数值,表明低光照图像经本文算法增强后图像质量得到明显提升.从图2和图3发现,本文算法也提升视觉效果的表现力.由表2的LOL 数据集上结果可知,在PSNR 指标上,本文算法总体上优于先进算法.根据文献[21]㊁文献[30]和文献[31]的研究,PSNR 指标因为容易计算,被广泛用于评估图像,但其计算是基于误差敏感度,在评估中常出现与人类感知系统不一致的现象,因此与图像主观效果结合分析能更好地体现图像质量.结合图2和图3的分析,GLADNet 的增强图像饱和度较低,存在颜色失真现象.文献[15]算法使图像过曝光.本文算法在Retinex⁃Net 基础上显著降低图像噪声,保留图像丰富结构信息,相比其它方法,视觉效果更佳,符合人类的视觉感知系统.在LOL 数据集上,对比大部分算法,本文取得与参考图像相近的NIQE 数值,表明本文算法的增强结果更接近参考图像.DICM㊁MEF 数据集没有正常光照图作为参照.本文只采用盲图像质量评估指标(NIQE㊁PIQE)评估各算法.在PIQE 指标上,本文算法取得最优值.对于NIQE,虽然未取得较好优势,但相比Retinex⁃Net,本文算法取得更好的增强结果.综上所述,虽然本文算法未在所有指标上取得最优结果,但仍有较高优势.在与人类视觉感知系统有较好相关性的SSIM 指标及噪声抑制和避免过曝能力上,本文算法最优.表2　各算法在3个数据集上的客观评估结果Table 2　Objective evaluation results of different algorithms on 3datasets 算法LOL 数据集SSIMUQI PSNRNIQEDICM 数据集PIQE NIQEMEF 数据集PIQE NIQESRIE0.4980.48211.8557.28716.95 3.89810.70 3.474LIME 0.6010.78916.8348.37815.60 3.8319.12 3.716GLADNet 0.7030.87919.718 6.47514.85 3.6817.96 3.360MBLLEN0.7040.82517.5633.58412.293.27012.043.322EnlightenGAN 0.6580.80817.483 4.68414.613.5627.863.221文献[15]算法0.7120.86019.150 4.79316.21 4.71811.78 4.361Retinex⁃Net 0.5590.87916.7749.73014.16 4.41511.90 4.480本文算法0.7200.88018.5294.49010.11 3.9607.77 3.820参考图像1--4.253---- 由图2可见,SRIE㊁LIME㊁EnlightenGAN 的增亮程度有限,增强结果偏暗.GLADNet㊁MBLLEN 改变图像饱和度,降低图像视觉效果.相比Retinex⁃Net,本文算法的增强结果图噪声水平较低,可保持图像原有的色彩.由图3可见,SRIE 增强程度远不足人类视觉需求,在图3的图像A 中,未展示其增强结果.在图3中,根据人眼视觉系统,首先能判断LIME㊁GLADNet㊁MBLLEN㊁EnlightenGAN 对人脸的增亮程度仍不足,GLADNet㊁MBLLEN 分别存在饱和度过低和过高现象.而文献[15]算法㊁Retinex⁃Net㊁本文算法能较好观察到人脸细节,但同时从左下角细节图可见,Retinex⁃Net 人脸的边缘轮廓对比度过强.从颜色上进一步分析可知,文献[15]算法亮度过度增强,导致图像颜色失真,如天空的颜色对比输入图像色调偏白㊁曝光难以观察远处的景物等.同样观察图3中图像B 右下角细节,经过分析可得,文献[15]算法㊁Retinex⁃Net㊁本文算法增强程度满足人类视觉需求,但文献[15]算法过曝光,从Retinex⁃Net 细节图可见人物服饰失真.对比其它算法,本文算法结果的亮度适中,图像具有丰富细节,避免光照伪影与过曝光现象.另外,从图3中图像C㊁D 可见,LIME㊁GLAD⁃Net㊁MBLLEN㊁EnlightenGAN 没能较好处理局部暗48模式识别与人工智能(PR&AI) 第34卷区,如笔记本㊁右下角拱门窗户区域仍未增亮,导致难以识别边缘细节.而文献[15]算法饱和度发生变化.Retinex⁃Net存在伪影和噪声等问题.本文算法不仅可增强图像的亮度,减小噪声,还保留图像的细节,图像效果更有利于高级视觉系统的识别或检测.综合上述分析发现,本文算法对低光照图像增强效果更优.3　结束语针对Retinex⁃Net噪声较大㊁颜色失真问题,本文提出改进Retinex⁃Net的低光照图像增强算法.算法在分解网络采用浅层上下采样结构及去噪损失,在增强网络嵌入注意力机制模块和颜色损失.实验表明,本文算法不仅能增强图像亮度,而且能显著降低噪声,并取得较优结果.本文算法较好地处理亮度增强过程中无法避免的噪声问题,兼顾提升亮度和降低噪声任务,可给未来研究图像多属性增强提供思路,如低光增强㊁去噪㊁颜色恢复㊁去模糊等多任务同步进行.今后研究重心将是实现图像多属性同步增强.同时扩展该研究网络结构到其它高级视觉任务中,作为图像预处理模块,期望实现网络的端到端训练.参考文献[1]HU X,MA P R,MAI Z H,et al.Face Hallucination from Low Quality Images Using Definition⁃Scalable Inference.Pattern Reco⁃gnition,2019,94:110-121.[2]陈琴,朱磊,后云龙,等.基于深度中心邻域金字塔结构的显著目标检测.模式识别与人工智能,2020,33(6):496-506. 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收稿日期：２０２０ ０３ １４；修回日期：２０２０ ０５ ０６ 基金项目：国家自然科学基金资助项目（９１７４６１０７） 作者简介：赵宇欣（１９９５ ），女，山西晋中人，硕士研究生，主要研究方向为机器学习、深度学习、计算机视觉（ｚｈａｏｙｕｘｉｎ＿ａｌｉｃｅ＠ｔｊｕ．ｅｄｕ．ｃｎ）；王冠（１９９２ ），女，内蒙古呼伦贝尔人，博士研究生，主要研究方向为深度学习、数学物理反问题．基于生成式对抗网络的画作图像合成方法赵宇欣，王　冠（天津大学数学学院，天津３００３５４）摘　要：画作图像合成旨在将两个不同来源的图像分别作为前景和背景融合在一起，这通常需要局部风格迁移。

现有算法过程繁琐且耗时，不能做到实时的图像合成。

针对这一缺点，提出了基于生成式对抗网络（ｇｅｎｅｒａｔｉｖｅａｄｖｅｒｓａｒｉａｌｎｅｔ，ＧＡＮ）的前向生成模型（ＰａｉｎｔｅｒＧＡＮ）。

ＰａｉｎｔｅｒＧＡＮ的自注意力机制和Ｕ Ｎｅｔ结构控制合成过程中前景的语义内容不变。

同时，对抗学习保证逼真的风格迁移。

在实验中，使用预训练模型作为ＰａｉｎｔｅｒＧＡＮ的生成器，极大地节省了计算时间和成本。

实验结果表明，比起已有方法，ＰａｉｎｔｅｒＧＡＮ生成了质量相近甚至更好的图像，生成速度也提升了４００倍，在解决局部风格迁移问题上是高质量、高效率的。

关键词：图像风格迁移；生成对抗网络；图像合成；自注意力机制中图分类号：ＴＰ３９１ ４１ 文献标志码：Ａ 文章编号：１００１ ３６９５（２０２１）０４ ０４７ １２０８ ０４ｄｏｉ：１０．１９７３４／ｊ．ｉｓｓｎ．１００１ ３６９５．２０２０．０３．００８２ＰａｉｎｔｅｒｌｙｉｍａｇｅｃｏｍｐｏｓｉｔｉｏｎｂａｓｅｄｏｎｇｅｎｅｒａｔｉｖｅａｄｖｅｒｓａｒｉａｌｎｅｔＺｈａｏＹｕｘｉｎ，ＷａｎｇＧｕａｎ（ＳｃｈｏｏｌｏｆＭａｔｈｅｍａｔｉｃｓ，ＴｉａｎｊｉｎＵｎｉｖｅｒｓｉｔｙ，Ｔｉａｎｊｉｎ３００３５４，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ｐａｉｎｔｅｒｌｙｉｍａｇｅｃｏｍｐｏｓｉｔｉｎｇａｉｍｓｔｏｈａｒｍｏｎｉｚｅａｆｏｒｅｇｒｏｕｎｄｉｍａｇｅｉｎｓｅｒｔｅｄｉｎｔｏａｂａｃｋｇｒｏｕｎｄｐａｉｎｔｉｎｇ，ｗｈｉｃｈｉｓｄｏｎｅｂｙｌｏｃａｌｓｔｙｌｅｔｒａｎｓｆｅｒ．Ｔｈｅｃｈｉｅｆｄｒａｗｂａｃｋｏｆｔｈｅｅｘｉｓｔｉｎｇｍｅｔｈｏｄｓｉｓｔｈｅｈｉｇｈｃｏｍｐｕｔａｔｉｏｎａｌｃｏｓｔ，ｗｈｉｃｈｍａｋｅｓｒｅａｌ ｔｉｍｅｏｐｅｒａｔｉｏｎｄｉｆｆｉｃｕｌｔ．Ｔｏｏｖｅｒｃｏｍｅｔｈｉｓｄｒａｗｂａｃｋ，ｔｈｉｓｐａｐｅｒｐｒｏｐｏｓｅｄａｆｅｅｄ ｆｏｒｗａｒｄｍｏｄｅｌｂａｓｅｄｏｎｇｅｎｅｒａｔｉｖｅａｄｖｅｒｓａｒｉａｌｎｅｔ ｗｏｒｋ（ＧＡＮ），ｃａｌｌｅｄＰａｉｎｔｅｒＧＡＮ．ＰａｉｎｔｅｒＧＡＮｉｎｔｒｏｄｕｃｅｄａｓｅｌｆ ａｔｔｅｎｔｉｏｎｎｅｔｗｏｒｋａｎｄａＵ Ｎｅｔｔｏｃｏｎｔｒｏｌｔｈｅｓｅｍａｎｔｉｃｃｏｎｔｅｎｔｉｎｔｈｅｇｅｎｅｒａｔｅｄｉｍａｇｅ．Ｍｅａｎｗｈｉｌｅ，ａｄｖｅｒｓａｒｉａｌｌｅａｒｎｉｎｇｇｕａｒａｎｔｅｅｄａｆａｉｔｈｆｕｌｔｒａｎｓｆｅｒｏｆｓｔｙｌｅ．ＰａｉｎｔｅｒＧＡＮａｌｓｏｉｎｔｒｏｄｕｃｅｄａｐｒｅ ｔｒａｉｎｅｄｎｅｔｗｏｒｋｗｉｔｈｉｎｔｈｅｇｅｎｅｒａｔｏｒｔｏｅｘｔｒａｃｔｆｅａｔｕｒｅｓ．ＴｈｉｓａｌｌｏｗｅｄＰａｉｎｔｅｒＧＡＮｔｏｄｒａｍａｔｉｃａｌｌｙｒｅｄｕｃｅｔｒａｉｎｉｎｇ ｔｉｍｅａｎｄｓｔｏｒａｇｅ．Ｅｘｐｅｒｉｍｅｎｔｓｓｈｏｗｔｈａｔ，ｃｏｍｐａｒｅｄｔｏｓｔａｔｅ ｏｆ ａｒｔｍｅｔｈｏｄｓ，ＰａｉｎｔｅｒＧＡＮｇｅｎｅｒａｔｅｄｉｍａｇｅｓｈｕｎｄｒｅｄｓｏｆｔｉｍｅｓｆａｓｔｅｒｗｉｔｈｃｏｍｐａｒａｂｌｅｏｒｓｕｐｅｒｉｏｒｑｕａｌｉｔｙ．Ｔｈｅｒｅｆｏｒｅ，ｉｔｉｓｅｆｆｅｃｔｉｖｅａｎｄｅｆｆｉｃｉｅｎｔｆｏｒｌｏｃａｌｓｔｙｌｅｔｒａｎｓｆｅｒ．Ｋｅｙｗｏｒｄｓ：ｉｍａｇｅｓｔｙｌｅｔｒａｎｓｆｅｒ；ＧＡＮ；ｉｍａｇｅｃｏｍｐｏｓｉｔｉｎｇ；ｓｅｌｆ ａｔｔｅｎｔｉｏｎ０　引言图像合成属于图像变换问题，目的是通过模型将一个简单的粘贴合成图像转变成一个融合为一体的图像。