Semantic Guidance Learning for High-Resolution Non-homogeneous Dehazing.

High-resolution non-homogeneous dehazing aims to generate a clear image from a 4000 × 6000 image with non-homogeneous haze. To the best of our knowledge, this task is a new challenge that was not addressed in the previous literature. To address this issue, we propose semantic-guided loss functions for high-resolution non-homogeneous dehazing. We find semantic information contains strong texture and color prior. Thus, we proposed to adopt the pre-trained model to generate the semantic mask to guide the neural network during the training phase. On the other hand, to handle the non-homogeneous dehazing process in the high-resolution scenario, we adjust the kernel size of the model to increase the receptive field. Furthermore, to deal with the different image sizes during the training and the testing phase, several post-processing methods are applied to improve the high-resolution non-homogeneous dehazing. Several experiments performed on challenging benchmark show that the proposed model achieves competitive performance in the NTIRE 2023 HR NonHomogeneous Dehazing Challenge.