Why cnn is better for under water image enhancement using machine learning?
Best insight from top research papers
Convolutional Neural Networks (CNNs) are superior for underwater image enhancement using machine learning due to their ability to address specific challenges in underwater photography. CNNs excel in extracting features that target issues like motion blur, low brightness, and color deviation in underwater images. Additionally, CNN structures like the 'flower convolution' and 'dewater pooling' enhance the perceptual field, retain local features, and highlight salient features effectively. Moreover, utilizing pre-trained CNN models like Resnet50 improves feature extraction capabilities and classification accuracy for underwater image enhancement tasks. The experimental results from various studies demonstrate that CNN-based models outperform traditional methods, offering superior image enhancement results in terms of denoising, deblurring, and color deviation removal.
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| Papers (5) | Insight |
|---|---|
| CNN is effective for underwater image enhancement due to its ability to learn complex features, enabling color restoration and image enhancement in underwater environments, as demonstrated in the research. | |
| CNN is superior for underwater image enhancement due to its feature extraction capabilities and the EEMRVFL classifier, which collectively improve classification accuracy significantly in underwater environments. | |
| CNN is effective for underwater image enhancement due to its 'flower convolution' and 'dewater pooling' structures, attention mechanism, multi-dimensional feature fusion, and adaptive loss function, improving image quality significantly. | |
| CNN is effective for underwater image enhancement due to its ability to target issues like motion blur, low brightness, and color deviation, as demonstrated in the ECO-GAN method. | |
| The CNN model in underwater image enhancement excels due to its 'flower convolution' and 'dewater pooling' structures, attention mechanism, multi-dimensional feature fusion, and adaptive loss function, enhancing image quality effectively. |
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