Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
This work addresses image quality enhancement for computer vision applications, but it appears incremental as it builds on existing U-Net architecture with specific modifications.
The paper tackles the problem of image dehazing by proposing a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion, which achieves favorable performance against state-of-the-art methods on benchmark datasets and real-world images.
In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. The proposed method is designed based on two principles, boosting and error feedback, and we show that they are suitable for the dehazing problem. By incorporating the Strengthen-Operate-Subtract boosting strategy in the decoder of the proposed model, we develop a simple yet effective boosted decoder to progressively restore the haze-free image. To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme. We show that the dense feature fusion module can simultaneously remedy the missing spatial information from high-resolution features and exploit the non-adjacent features. Extensive evaluations demonstrate that the proposed model performs favorably against the state-of-the-art approaches on the benchmark datasets as well as real-world hazy images.