Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN

TL;DR

This paper introduces PG-CycleGAN, a novel unpaired image defogging method specifically designed for overwater scenes, utilizing prior maps and advanced modules to improve fog removal and detail preservation.

Contribution

The paper presents a new unpaired defogging framework with prior map guidance, UIM, and LRC modules tailored for overwater images, addressing limitations of existing methods.

Findings

Outperforms state-of-the-art defogging methods in overwater scenes.

Effectively suppresses sky and emphasizes water objects.

Reduces artifacts and detail loss in fog removal.

Abstract

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.