Semantic Segmentation of Degraded Images Using Layer-Wise Feature Adjustor

Kazuki Endo, Masayuki Tanaka, Masatoshi Okutomi; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 3205-3213

Semantic segmentation of degraded images is important for practical applications such as autonomous driving and surveillance systems. The degradation level, which represents the strength of degradation, is usually unknown in practice. Therefore, the semantic segmentation algorithm needs to take account of various levels of degradation. In this paper, we propose a convolutional neural network of semantic segmentation which can cope with various levels of degradation. The proposed network is based on the knowledge distillation from a source network trained with only clean images. More concretely, the proposed network is trained to acquire multi-layer features keeping consistency with the source network, while adjusting for various levels of degradation. The effectiveness of the proposed method is confirmed for different types of degradations: JPEG distortion, Gaussian blur and salt&pepper noise. The experimental comparisons validate that the proposed network outperforms existing networks for semantic segmentation of degraded images with various degradation levels.