Minimising Gaussian noise from real time CCTV images using GAN

@article{1705971,
      author = {Pranava Aithal K S,  Akshay Prashant Hegde, Adyapadi Suraj},
      title = {Minimising Gaussian noise from real time CCTV images using GAN},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {12},
      pages = {384-393},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705971.pdf},
      abstract = {This paper describes a system that uses generative adversarial networks (GANs) to eliminate gaussian noise from CCTV images. Unwanted noise like gaussian noise frequently degrades the quality of images and makes them more difficult to interpret. In our approach, a discriminator network is used to direct the training of a generator network, whose job it is to produce denoised images from noisy inputs. This framework includes operations like picture enhancement, noise reduction, and evaluation with metrics like the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The resultant denoised images show enhanced visual quality and have potential uses in image analysis and computer vision.},
      keywords = {Generative Adversarial Networks (GANs), Image Interpretation, Noise Removal, Visual Quality Improvement},
      month = {June},
  }