Abstract:
We propose an underwater image enhancement algorithm that leverages both model- and learning-based approaches by unfolding an iterative algorithm. We first formulate the ...Show MoreMetadata
Abstract:
We propose an underwater image enhancement algorithm that leverages both model- and learning-based approaches by unfolding an iterative algorithm. We first formulate the underwater image enhancement task as a joint optimization problem, based on the image formation model with physical model and underwater-related priors. Then, we solve the optimization problem iteratively. Finally, we unfold the iterative algorithm so that, at each iteration, the optimization variables and regularizers for image priors are updated by closed-form solutions and learned deep networks, respectively. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art underwater image enhancement algorithms.
Date of Conference: 08-11 October 2023
Date Added to IEEE Xplore: 11 September 2023
ISBN Information:
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- IEEE Keywords
- Index Terms
- Underwater Image ,
- Underwater Image Enhancement ,
- Optimization Problem ,
- Deep Network ,
- Iterative Algorithm ,
- Optimization Variables ,
- Learning-based Approaches ,
- Joint Optimization Problem ,
- Convolutional Neural Network ,
- Image Quality ,
- Generative Adversarial Networks ,
- Regularization Parameter ,
- Natural Images ,
- Low Contrast ,
- Physical Constraints ,
- Penalty Parameter ,
- Color Channels ,
- Regular Function ,
- Background Light ,
- Transmission Map ,
- Undersea ,
- Image Processing Tasks ,
- Proximal Operator ,
- Color Distortion ,
- Inaccurate Model
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Underwater Image ,
- Underwater Image Enhancement ,
- Optimization Problem ,
- Deep Network ,
- Iterative Algorithm ,
- Optimization Variables ,
- Learning-based Approaches ,
- Joint Optimization Problem ,
- Convolutional Neural Network ,
- Image Quality ,
- Generative Adversarial Networks ,
- Regularization Parameter ,
- Natural Images ,
- Low Contrast ,
- Physical Constraints ,
- Penalty Parameter ,
- Color Channels ,
- Regular Function ,
- Background Light ,
- Transmission Map ,
- Undersea ,
- Image Processing Tasks ,
- Proximal Operator ,
- Color Distortion ,
- Inaccurate Model
- Author Keywords