# Zoom, Enhance! Measuring Surveillance GAN Up-sampling

arXiv: https://arxiv.org/abs/2108.09285 · HTML: https://mlsanity.com/paper/2108.09285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 21 of 21)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Understanding SSIM](https://mlsanity.com/paper/2006.13846/) (2020)
- [A Formal Evaluation of PSNR as Quality Measurement Parameter for Image Segmentation Algorithms](https://mlsanity.com/paper/1605.07116/) (2016)
- [iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks](https://mlsanity.com/paper/2006.11161/) (2020)
- [Formal Definition of QoE Metrics](https://mlsanity.com/paper/1607.00321/) (2016)
- [Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)](https://mlsanity.com/paper/1905.05373/) (2019)
- [CNNs for Surveillance Footage Scene Classification](https://mlsanity.com/paper/1809.02766/) (2018)

## Abstract (arXiv)

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.