Zoom, Enhance! Measuring Surveillance GAN Up-sampling
This work addresses image enhancement for surveillance applications, but it is incremental as it focuses on comparing existing methods and metrics.
The paper evaluated and compared CNN and GAN-based up-sampling techniques for security and surveillance, finding that DISTS is a stronger image quality assessment metric for GAN-based up-sampling in this domain.
Deep Neural Networks have been very successfully used for many computer vision and pattern recognition applications. While Convolutional Neural Networks(CNNs) have shown the path to state of art image classifications, Generative Adversarial Networks or GANs have provided state of art capabilities in image generation. In this paper we extend the applications of CNNs and GANs to experiment with up-sampling techniques in the domains of security and surveillance. Through this work we evaluate, compare and contrast the state of art techniques in both CNN and GAN based image and video up-sampling in the surveillance domain. As a result of this study we also provide experimental evidence to establish DISTS as a stronger Image Quality Assessment(IQA) metric for comparing GAN Based Image Up-sampling in the surveillance domain.