DehazeDNet: image dehazing via depth evaluation

Haze is a natural phenomenon that negatively affects image clarity and quality, posing challenges across various image-related applications. Traditional dehazing models often suffer from overfitting when trained on synthetic hazy-clean image pairs, which do not generalize well to real-world hazy conditions. To tackle this, recent methodologies have explored training models on unpaired data, better reflecting the variability encountered in natural scenes. This dual capability of CycleGAN is particularly beneficial for overcoming the overfitting issues associated with synthetic datasets. By incorporating CycleGAN into our DehazeDNet framework, we ensure that our dehazing model not only translates images effectively but also respects the physical characteristics of haze. Inspired by the D4 model, our approach includes a Depth Evaluation Block to estimate scene depth from images. Since haze density often correlates with scene depth, this depth information is crucial for accurate haze modeling. We utilize the U-Net architecture for the Depth Evaluation Block due to its proven efficiency in image-to-image translation tasks. To preserve the accuracy of the dehazed images, we incorporate an identity loss function into our model. Identity loss ensures that the dehazed output retains the essential characteristics of the input image. Our results demonstrate an increase in SSIM and PSNR compared to other unsupervised dehazing models, highlighting the efficiency of our method in maintaining image quality and details while removing haze.