The paper proposes a Physics-guided Parametric Augmentation Network (PANet) to address the challenge of the large domain gap between synthetic and real-world hazy images, which significantly limits the performance of deep dehazing models in practical settings.
PANet comprises a Haze-to-Parameter Mapper (HPM) to project hazy images into a parameter space and a Parameter-to-Haze Mapper (PHM) to map the resampled haze parameters back to hazy images. The estimated haze parameters, including pixel-wise haze density and atmospheric light, have physical meanings, allowing PANet to generate diverse realistic hazy images with various explainable haze conditions unseen in the training set.
The paper demonstrates that PANet can effectively augment the training data to enrich existing hazy image benchmarks, leading to significant performance improvements on state-of-the-art dehazing models across four real-world hazy image datasets. Extensive experiments validate the efficacy and generalizability of PANet.
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by Chih-Ling Ch... في arxiv.org 04-16-2024
https://arxiv.org/pdf/2404.09269.pdfاستفسارات أعمق