From CNN to GAN: The evolution of deep learning techniques in image processing
DOI:
https://doi.org/10.61173/a92jnh68Keywords:
Image Processing, deep learning, Convolutional Neural Network , Generative Adversarial NetworksAbstract
In recent years, the technique for image processing has developed to improve the visual effects of images. This paper introduces ways to express images and basic operations. For the rest part of this paper, we elaborate on the current techniques for image processing including CNNs, RNNs, and GANs. each of them has different benefits and drawbacks. This paper also have further introduction about the theory and structure of neural networks above, and the reason for creating GANs, which is two adversarial network to improve the visual effects.
References
[1] Muhammad Waqas and Usa Wannasingha Humphries. A critical review of RNN and LSTM variants in hydrological time series predictions[J]. ScienceDirect, 2024.
[2] Karuna R. Dongur Pushpa Tandekar, Shrawan Kumar Purve. Digital Image Processing: Its History and Application[J]. IJARCCE, 2022.
[3] Cheng Yu, Wenmin Wang, Roberto Bugiolacchi. Improving generative adversarial network inversion via fine-tuning GAN encoders[J]. ScienceDirect, 2024.
[4] Xiangwei Zheng, Lifeng Zhang, Chunyan Xu, Xuanchi Chen, Zhen Cui. An attribution graph-based interpretable method for CNNs[J]. ScienceDirect, 2024.
[5] Ismail Akg¨ul. A Pooling Method Developed for Use in Convolutional Neural Networks[J]. ScienceDirect, 2024.
[6] Mukul, Singh S, Nishi. The Origins of Digital Image Processing Application areas in Digital Image Processing Medical Images[J]. Ijert, 2018.
[7] B R Q A, C M U, B K K, et. al. Hyperspectral document image processing: Applications, challenges and future prospects[J]. ScienceDirect, 2019
[8] MD S P, MSc D D, MD S G, et. al. Kiosk 5R-FA-03 - Automated Cardiac MRI Plane Prescription Through Cnn-based Landmark Detection[C]: Journal of Cardiovascular Magnetic Resonance, ELSEVIER, 2024.
[9] A H L, A C Q, A C L, et. al. Chapter 18 - Generative adversarial network (GAN) assisted IoT search engine for disaster damage assessment[M]. Elsevier Shop, 2024.
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