Attentions in Deep Framework to Enhance Images Degraded by Non-Homogeneous Haze
Akash Dhedhi, Srimanta Mandal, Rajib Lochan Das · 2023
The availability of dehazing datasets has enabled various deep learning techniques to perform effectively on hazy images. Most of the developed frameworks focus on removing homogeneous haze. However, homogeneous-centric methods produce sub-optimal results on non-homogeneous haze. The primary reason is that the architectures devised to handle homogeneous haze fail to address the non-uniformity of haze in non-homogeneous case. The secondary reason is the unavailability of enough data for the non-homogeneous scenario. Although many works cite the lack of data as a primary concern for poor performance, we find that the results are sub-standard even if the homogeneous-centric networks are trained with non-homogeneous data. Hence, there is a requirement for a network architecture that can handle non-homogeneous haze in a better way. In this work, we propose to use multiple attention mechanisms in parallel along with pre-trained ConvNeXt blocks. Specifically, we use pixel, channel, and residual channel attention mechanisms. Pixel attention can complement channel attention in dealing with space-variant haze when connected in parallel. On the other hand, residual channel attention fetches hazy image-related features and caters to better information flow toward the output. The proposed method by concatenating the attention-based features yields better results than the existing approaches.