Summary of Blind Underwater Image Restoration Using Co-operational Regressor Networks, by Ozer Can Devecioglu et al.
Blind Underwater Image Restoration using Co-Operational Regressor Networks
by Ozer Can Devecioglu, Serkan Kiranyaz, Turker Ince, Moncef Gabbouj
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| 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 abstract proposes a novel machine learning model, Co-Operational Regressor Networks (CoRe-Nets), designed for underwater image restoration. The model consists of two co-operating networks: the Apprentice Regressor (AR) and the Master Regressor (MR). AR is responsible for image transformation, while MR evaluates the Peak Signal-to-Noise Ratio (PSNR) of the images generated by AR and feeds it back to AR. CoRe-Nets are built on Self-Organized Operational Neural Networks (Self-ONNs), which offer superior learning capabilities through nonlinearity modulation in kernel transformations. The model achieves state-of-art restoration performance with reduced computational complexity, often surpassing ground truth visual quality. The proposed approach is demonstrated on the Large Scale Underwater Image (LSUI) dataset and implemented in PyTorch for public sharing on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make underwater images clearer by using special machine learning models called CoRe-Nets. These models are good at fixing blurry or distorted images that come from exploring underwater environments. The model has two parts: one part helps fix the image, and another part makes sure the fixed image looks really good compared to the original. This approach is tested on a big dataset of underwater images and shows great results, making it useful for scientists studying marine life, archaeologists searching for artifacts, and people maintaining underwater infrastructure. |
Keywords
» Artificial intelligence » Machine learning
