2018/10/22 by N. Bharath Raj, Bharath Raj N., N., Bharath Raj +3
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image Enhancement Techniques #Image and Video Processing (eess.IV) #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1810.09479
Accepted for the WiSPNET 2020 conference. Please refer to the GitHub repository for information on updates to the paper: https://github.com/thatbrguy/Dehaze-GAN
openalex publication_date 2018/10/22 · openalex created_date 2018/10/26 · arxiv created 2020/08/29 · arxiv updated 2020/09/01 · openalex updated_date 2026/07/28
Traditional methods to remove haze from images rely on estimating a transmission map. When dealing with single images, this becomes an ill-posed problem due to the lack of depth information. In this paper, we propose an end-to-end learning based approach which uses a modified conditional Generative Adversarial Network to directly remove haze from an image. We employ the usage of the Tiramisu model in place of the classic U-Net model as the generator owing to its higher parameter efficiency and performance. Moreover, a patch based discriminator was used to reduce artefacts in the output. To further improve the perceptual quality of the output, a hybrid weighted loss function was designed and used to train the model. Experiments on synthetic and real world hazy images demonstrates that our model performs competitively with the state of the art methods.