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基于图像转换和随机向量的红外图像生成方法研究
Alternative TitleResearch on Infrared Image Generation Method Based on Image Translation and Random Vector
陈佛计
Department光电信息技术研究室
Thesis Advisor王恩德
Keyword生成对抗网络 红外图像生成 图像转换 生成图像评估 生成模型
Pages59页
Degree Discipline模式识别与智能系统
Degree Name硕士
2020-05-26
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract随着深度神经网络技术的发展,人工智能技术在很多视觉任务中有着优异的表现,但是深度神经网络这种优良的性能,在很大程度上依赖于海量的数据。在实际工程项目中,相较于可见光图像,红外图像不易受到光照变化的影响,所以在无人系统或者是其他军事领域的视觉任务中,红外图像是一种很好的选择。但是实际很多场景红外图像的数量很少,难以支撑一个模型的训练,因此本文重点研究如何获取更多的、和真实红外图像服从同一分布的样本。生成红外图像的方法有很多,基于生成对抗网络生成红外图像的方法是目前相对比较新、比较热门的途径之一。而本文以实际工程项目中的应用需求为背景,采用深度神经网络和对抗网络模型相结合的深度生成模型来做红外图像的生成。首先,在有大量场景可见光图像的前提下,改进基于图像转换的方法来生成红外图像。由于彩色图像和红外图像在高维空间中的语义信息是一致的,在该模型中采用编解码的网络结构来实现图像的转换,编码器主要提取输入图像的高维语义信息,而解码器负责将高维语义信息映射成红外图像,最后通过实验证明该方法可以提高生成红外图像的质量。其次,为了能控制生成器生成指定类型的红外图像,提出了基于先验知识和生成对抗网络直接生成红外图像的方法。该方法采用自注意力机制模块来更好的发现图像块与块之间的联系,更好的辅助生成红外图像。并且基于类标签先验知识和域分类损失约束,来让生成器去学着生成指定类型的红外图像。最后通过实验证明了该方法的有效性,并且该方法在实际生成红外图像数据的时候会更加实用。本文的研究主要针对实际项目中小样本的问题,对如何获取更多样本数据的方法进行了深入的探讨和研究。并且提出了两种生成红外图像的方法,通过实验验证了方法的可行性,从而为实际应用提供了一种可能的途径。
Other AbstractWith the development of deep neural network technology, artificial intelligence technology has excellent performance in many visual tasks. However, the excellent performance of deep neural networks relies heavily on massive data. In actual engineering projects, infrared images are less susceptible to changes in illumination than visible light images. Therefore, in unmanned systems or other vision tasks in the field of military, infrared images are a good choice. However, the actual number of infrared images in many scenes is very small, and it is difficult to support the training of a deep neural network model. Therefore, this paper focuses on how to obtain more samples that follow the same distribution as real infrared images. There are many methods for generating infrared images, such as simulation-based methods, variational autoencoder-based methods, and adversarial network-based methods. The method of generating infrared images based on the generated adversarial network is one of the relatively novel and popular methods at present. Based on the application requirements in actual engineering projects, this paper uses a deep generation model built by deep neural networks and adversarial networks model to generate infrared images. Firstly, on the premise of a large number of scene visible light images, an infrared image generation method based on image translation is improved. Since the semantic information of color images and infrared images in the high-dimensional space is consistent, the encoder-decoder network structure was employed in this model to realize the image translation from color field to infrared field. The encoder mainly extracts the high-dimensional semantic information of the input image, and the decoder is responsible for mapping the high-dimensional semantic information into an infrared image. Finally, experiments prove that the image translation method can improve the quality of generated infrared images. Secondly, in order to control the generator to generate the specified type of infrared image, a method of directly generating infrared images based on prior knowledge and the adversarial network is proposed. This method uses a self-attention mechanism module to better discover the internal connections in the image and better assist in generating infrared images. And based on prior knowledge of class labels and domain classification loss constraints, the generator can learn to generate a specified type of infrared image. Finally, the effectiveness of the method is validated through experiments, and the method will be more practical when generating infrared images. The research in this paper is mainly aimed at the problem of small samples in actual projects and has conducted in-depth discussion and research on how to obtain more sample data. Besides, two methods for generating infrared images are proposed. And the feasibility of the method was verified through experiments, which provides a possible way for practical application.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/27128
Collection光电信息技术研究室
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
陈佛计. 基于图像转换和随机向量的红外图像生成方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2020.
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