DC-GAN with feature attention for single image dehazing

In recent years, the frequent occurrence of smog weather has affected people's health and has also had a major impact on computer vision application systems. Images captured in hazy environments suffer from quality degradation and other issues such as color distortion, low contrast, and lack of detail. This study proposes an end-to-end, adversarial neural network-based dehazing technique called DC-GAN that combines Dense and Residual blocks efficiently for improved dehazing performance. In addition, it also consists of channel attention and pixel attention, which can offer more versatility when dealing with different forms of data. The Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used as an enhancement method to correct the short-comings in the original GAN's cost function and create an improvised loss. Based on the experiment results, the algorithm used in this research can generate sharp images with high image quality. The processed images were simultaneously analyzed using the objective evaluation metrics Peak Signal-to-Noise Ratio and Structural Similarity. The findings from our experiment demonstrate that the dehazing effect is favorable compared to other state-of-the-art dehazing algorithms.