Domain Adaptation in Image Dehazing: Exploring the Usage of Images From Virtual Scenarios
Abstract:This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image haze removal problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator; these synthetic images contain different densities of haze, which are used for training the model that is later adapted to any real scenario. The adaptation process requires just a few images to fine-tune the model parameters. The proposed strategy allows overcoming the limitation of tra… Show more
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