LAUSR

In the past decade, significant research effort has been directed toward developing single-image dehazing algorithms. Despite this effort, dehazing continues to present a challenge, particularly in complex real-world cases. Indeed, it is an ill-posed problem because scene transmission depends on unknown and nonhomogeneous depth information. This paper proposes a novel end-to-end adaptive enhancement dehazing network (AED-Net) to recover clean scenes from hazy images. We evaluate it quantitatively and qualitatively against several state-of-the-art methods on three commonly used dehazing benchmark datasets as well as hazy real-world images. Moreover, we evaluated it against the top-scoring methods of the Codalab NTIRE 2021 competition based on the dehazing challenge dataset. Extensive computer simulations demonstrated that AED-Net outperforms state-of-the-art single-image haze removal algorithms in terms of PSNR, SSIM, and other key metrics. Furthermore, it improves image texture, detail edges, boosts image contrast and color fidelity. Finally, AED-Net is more effective under complex real-world conditions.