Abstract: Although deep learning-based semantic segmentation methods have achieved excellent results on traditional driving datasets, low-quality images captured under foggy conditions remain challenging. To address this issue, this paper proposes a learnable image filter (LIF) module, aiming to leverage the intrinsic characteristics of driving scene images under varying fog densities to improve semantic segmentation in foggy driving conditions. The LIF module consists of a hyperparameter prediction module (HPM) and an image filtering module (IFM), where the hyperparameters of the filter in the IFM are predicted by the HPM. This paper jointly learns the HPM and the semantic segmentation network in an end-to-end manner, ensuring that the HPM can learn appropriate IFM parameters to enhance images for segmentation in a weakly supervised manner. Taking DeepLabV3+, PSPNet, and RefineNet as baselines, respectively, experiments were conducted on a mixed dataset of Cityscapes and Foggy Cityscapes. The mean intersection over union (MIoU) scores of the baselines with the learnable image filter module are 63.14%, 60.45%, and 61.41%, representing improvements of 3.03%, 1.52%, and 1.69% over the baselines, respectively. The experimental results demonstrate the effectiveness and generality of the proposed module.

Key words: foggy image；image semantic segmentation, image filter, convolutional neural network, image processing