Cycle-Iterative Image Dehazing Based on Noise Evolution

Benefiting from the prevalence of machine learning theory, deep learning-based image dehazing algorithms have made remarkable progress. However, limited by (1) the incompleteness of symmetric datasets, (2) insufficient extraction of deep priors, and (3) the excessive scale of the network, such algorithms have poor generalization ability on real-world datasets and lack real-time performance. To address these issues, this paper proposes a noise evolution-based cycle-iterative dehazing algorithm. In our method, the noise evolution in each iteration includes an atmospheric scattering model (ASM)-based dehazing module, a random haze addition module, and a Retinex-based inverse enhancement module. More specifically, the ASM-based image dehazing module initially clarifies hazy images by extracting haze-related features according to the ASM. The random haze addition module combines the depth-related parameters extracted by the previous module with a random adjustment or an assignment mechanism to expand the samples, thereby addressing the problem of data shortage. The Retinex-based inverse enhancement module is introduced to mine “depth” features related to illumination, aiming to ensure the extraction of richer priors from the Retinex model. It is worth noting that both the dehazing module and the inverse enhancement module use the low-complexity U-Net as the main backbone, and the random haze addition module only involves simple operation. Therefore, it effectively suppresses the deployment scale and computational complexity of our algorithm. Experiments reveal that the proposed algorithm not only robustly restores hazy images but also exhibits promising advantages in terms of running time and the scale of network parameters.