We propose a model-driven deep learning approach to underwater image enhancement that can take advantage of both model- and learning-based approaches. We first formulate a joint optimization problem with physical priors to estimate the transmission map and latent clear image. Then, we solve the optimization problem iteratively. At each iteration, the optimization variables and image priors are updated by closed-form solutions and learned deep neural networks, respectively. Experimental results show that the proposed algorithm outperforms state-of- the-art underwater image enhancement algorithms.
We propose a low-light image enhancement algorithm that learns channel-wise transformation functions. First, we develop a lightweight network, called the transformation function estimation network (TFE-Net), to predict the channel-wise transformation functions. TFE-Net learns to generate the transformation functions by considering both the global and local characteristics of the input image. Then, we obtain enhanced images by performing channel-wise intensity transformation. Experimental results show that the proposed algorithm provides higher image quality than conventional algorithms.
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