A comprehensive survey of recent trends in deep learning for digital images augmentation
By Nour Eldeen M. Khalifa, Mohamed Loey, S. Mirjalili. Artificial Intelligence Review, 2021
381 citations recorded by the source corpus.
Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/902f154bffb1a0b725eb682699d2a65c5ce152d8
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- A Comprehensive Survey of Image Augmentation Techniques for Deep Learning (2022, methodology): “Finally, unlike two related studies [6, 7], our survey encompasses more recent yet effective image augmentation algorithms such as instance level multiple image mixing, as well as comprehensive analysis of model-based methods.”
- Data Augmentation in Classification and Segmentation: A Survey and New Strategies (2023, background): “In addition, they may result in crucial image information loss, particularly when the feature is a colorimetric feature capable of differentiating different dataset categories [44].”