Single Image Dehazing Using A Tiramisu Auto- Encoder

This paper presents a comprehensive approach to address the challenge of dehazing on-road images by synthesising datasets and training advanced deep learning models. Leveraging Pix2Pix GAN and introducing a novel Tiramisu Autoencoder architecture, the research endeavours to overcome the scarcity of real-world data through data augmentation and synthesis. The Pix2Pix GAN is modified to generate realistic haze, while the Tiramisu Autoencoder is used to de-haze the images. Challenges in data collection, including the absence of on-road data and time-series data, are elucidated. The novel architecture demonstrates promising results on benchmark datasets. The research boasts advances in both data synthesis and image de-hazing.