Summary of Advanced Underwater Image Quality Enhancement Via Hybrid Super-resolution Convolutional Neural Networks and Multi-scale Retinex-based Defogging Techniques, by Yugandhar Reddy Gogireddy et al.
Advanced Underwater Image Quality Enhancement via Hybrid Super-Resolution Convolutional Neural Networks and Multi-Scale Retinex-Based Defogging Techniques
by Yugandhar Reddy Gogireddy, Jithendra Reddy Gogireddy
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study proposes a hybrid approach to address underwater image degradation caused by light scattering, absorption, and fog-like particles. The method combines Multi-Scale Retinex (MSR) defogging methods with Super-Resolution Convolutional Neural Networks (SRCNN) to enhance the clarity, contrast, and color restoration of underwater images. The MSR algorithm reduces uneven lighting and fogging, while the SRCNN component improves spatial resolution. Extensive experiments on real-world underwater datasets demonstrate notable advances in sharpness, visibility, and feature retention using metrics like Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR). This approach has implications for real-time underwater applications such as marine exploration, underwater robotics, and autonomous underwater vehicles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Underwater images are often degraded due to light scattering, absorption, and fog-like particles. Researchers developed a new method to improve these images using a combination of computer vision techniques. They used two main methods: Multi-Scale Retinex (MSR) to reduce uneven lighting and fogging, and Super-Resolution Convolutional Neural Networks (SRCNN) to enhance spatial resolution. This hybrid approach was tested on real-world underwater datasets and showed significant improvements in image quality. The results have the potential to improve marine exploration, underwater robotics, and autonomous underwater vehicles. |
Keywords
» Artificial intelligence » Super resolution
