Synthetic Image Generation Using Deep Learning: A Systematic Literature Review

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aisha Zulfiqar, Sher Muhammad Daudpota, Ali Shariq Imran, Zenun Kastrati, Mohib Ullah, Suraksha Sadhwani
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Abstract

The advent of deep neural networks and improved computational power have brought a revolutionary transformation in the fields of computer vision and image processing. Within the realm of computer vision, there has been a significant interest in the area of synthetic image generation, which is a creative side of AI. Many researchers have introduced innovative methods to identify deep neural network-based architectures involved in image generation via different modes of input, like text, scene graph layouts and so forth to generate synthetic images. Computer-generated images have been found to contribute a lot to the training of different machine and deep-learning models. Nonetheless, we have observed an immediate need for a comprehensive and systematic literature review that encompasses a summary and critical evaluation of current primary studies' approaches toward image generation. To address this, we carried out a systematic literature review on synthetic image generation approaches published from 2018 to February 2023. Moreover, we have conducted a systematic review of various datasets, approaches to image generation, performance metrics for existing methods, and a brief experimental comparison of DCGAN (deep convolutional generative adversarial network) and cGAN (conditional generative adversarial network) in the context of image generation. Additionally, we have identified applications related to image generation models with critical evaluation of the primary studies on the subject matter. Finally, we present some future research directions to further contribute to the field of image generation using deep neural networks.

Abstract Image

使用深度学习生成合成图像:系统性文献综述
深度神经网络的出现和计算能力的提高给计算机视觉和图像处理领域带来了革命性的变革。在计算机视觉领域,人们对合成图像生成这一人工智能的创造性领域产生了浓厚的兴趣。许多研究人员引入了创新方法,通过不同的输入模式(如文本、场景图布局等)来识别参与图像生成的基于深度神经网络的架构,从而生成合成图像。人们发现,计算机生成的图像对不同机器和深度学习模型的训练有很大帮助。然而,我们注意到,目前急需一份全面、系统的文献综述,其中包括对当前主要研究的图像生成方法进行总结和批判性评估。为此,我们对 2018 年至 2023 年 2 月期间发表的合成图像生成方法进行了系统的文献综述。此外,我们还对各种数据集、图像生成方法、现有方法的性能指标进行了系统回顾,并在图像生成方面对 DCGAN(深度卷积生成对抗网络)和 cGAN(条件生成对抗网络)进行了简要的实验比较。此外,我们还确定了与图像生成模型相关的应用,并对该主题的主要研究进行了批判性评估。最后,我们提出了一些未来的研究方向,以进一步促进使用深度神经网络生成图像领域的发展。
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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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