# Generating High Quality Visible Images from SAR Images Using CNNs

arXiv: https://arxiv.org/abs/1802.10036 · HTML: https://mlsanity.com/paper/1802.10036/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 9 of 9)

- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation](https://mlsanity.com/paper/2411.10788/) (2024)
- [A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2104.09350/) (2021)
- [Joint optimization of system design and reconstruction in MIMO radar imaging](https://mlsanity.com/paper/2110.03218/) (2021)
- [TIC: Text-Guided Image Colorization](https://mlsanity.com/paper/2208.02843/) (2022)
- [SAR-to-Optical Image Translation via Thermodynamics-inspired Network](https://mlsanity.com/paper/2305.13839/) (2023)
- [ViT-Inception-GAN for Image Colourising](https://mlsanity.com/paper/2106.06321/) (2021)
- [MMC: Multi-Modal Colorization of Images using Textual Descriptions](https://mlsanity.com/paper/2304.11993/) (2023)
- [Multi-task SAR Image Processing via GAN-based Unsupervised Manipulation](https://mlsanity.com/paper/2408.01553/) (2024)

## Abstract (arXiv)

We propose a novel approach for generating high quality visible-like images from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on a cascaded network of convolutional neural nets (CNNs) for despeckling and image colorization. The cascaded structure results in faster convergence during training and produces high quality visible images from the corresponding SAR images. Experimental results on both simulated and real SAR images show that the proposed method can produce visible-like images better compared to the recent state-of-the-art deep learning-based methods.