Enhancing Image Super-Resolution with Generative Adversarial Networks
Keywords:
Image Super-Resolution, Generative Adversarial Networks, Perceptual Loss, Deep Learning, Image ReconstructionAbstract
Image Super-Resolution (SR) is an important problem in computer vision, which aims to restore high-resolution (HR)images from their low-resolution (LR) versions. Producing highquality SR is important for many applications, such asmedicine, satellite images, video analysis and surveillance. Conventional SR methods (e.g., interpolation and optimization based algorithms) tend to fail in restoring fine textures and details, producing overly-smooth and blurred images
References
Dong, C., Loy, C. C., He, K., & Tang, X. (2014). Learning a deep convolutional network for image super-resolution. European Conference on Computer Vision (ECCV), 184-199. https://doi.org/10.1007/978-3-319-10593-2_13
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Published
2023-11-15
How to Cite
Gowrinath Jarugula. (2023). Enhancing Image Super-Resolution with Generative Adversarial Networks. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2532–2539. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4868
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