You Only Need Clear Images: Self-Supervised Single Image Dehazing

Abstract

Image hazing refers to adding haze to a clear image, which is important for improving the data amount and diversity of synthetic hazy images that are required to train deep image dehazing models. However, existing image hazing works generate hazy images from a given clear image with a single transmission map. This violates the fact that hazy images are diverse for a natural scene at different times. The domain shift issue between synthetic and real-world hazy images constrains the robustness of deep dehazing models when dealing with real-world hazy images. In this work, we propose an unsupervised haze generation work to synthesize multiple hazy images with diverse haze distributions from a clear image, which requires only an atmospheric scattering model without extra labeling information. Instead of estimating a transmission map from a clear image, we propose to customize the transmission maps by redefining the transmission function. In such a controllable way, hazy images with diverse haze distributions are generated, which avoids the labor-intensive collection of paired data and alleviates the common domain-shift issue of deep image dehazing. Incorporating the unsupervised hazy images generator, we also construct a generalizable self-supervised image dehazing (SSID) framework, where deep image dehazing models can be trained without any human annotations. Extensive experiments on real-world hazy images show that the proposed approach is superior to state-of-the-art unsupervised dehazing works, and achieves competitive performance with the supervised works. Moreover, the proposed SSID framework can be easily generalized to the existing deep dehazing models, greatly improving dehazing robustness on real-world hazy images.

Topics & Concepts