Enhanced Pix2Pix GAN for Visual Defect Removal in UAV-Captured Images
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Abstract (arXiv)
This paper presents a neural network that effectively removes visual defects from UAV-captured images. It features an enhanced Pix2Pix GAN, specifically engineered to address visual defects in UAV imagery. The method incorporates advanced modifications to the Pix2Pix architecture, targeting prevalent issues such as mode collapse. The suggested method facilitates significant improvements in the quality of defected UAV images, yielding cleaner and more precise visual results. The effectiveness of the proposed approach is demonstrated through evaluation on a custom dataset of aerial photographs, highlighting its capability to refine and restore UAV imagery effectively.
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References · 9
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- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
- 2017Improved Training of Wasserstein GANscited by 2,235
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2018Spectral Normalization for Generative Adversarial Networkscited by 1,636
- 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
- 2017Wasserstein GANcited by 639
- 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 363
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