Photo realistic synthetic dataset and multi-scale attention dehazing network

Deep learning is a powerful tool in the realm of low-level computer vision and has achieved significant success in image dehazing. However, previous works have predominantly focused on synthetic hazy images, thereby overlooking the inherent differences between real-world hazy images and their synthetic counterparts. These prior approaches face a performance decline when models trained with synthetic hazy images are applied to naturally hazy scenes. In this context, we propose a novel method aimed at minimizing the discrepancies between real and synthetic hazy images, thus enhancing the dehazing performance for real-world scenarios. Specifically, our approach includes refining the synthesized transmission map, which often misses details around object boundaries. Furthermore, we implement a technique that transfers the visual appearance of natural haze onto simulated images. Additionally, we introduce noise into the synthesized hazy images to enhance realism. To demonstrate the effectiveness of our dataset, we introduce a multi-scale attention dehazing network, which delivers state-of-the-art dehazing results. Extensive experiments robustly attest to the superior performance of our proposed method. We further conduct experiments to validate the efficacy of the proposed dataset in addressing the challenges posed by real-world haze.