MA-MFCNet: Mixed Attention-Based Multi-Scale Feature Calibration Network for Image Dehazing

Abstract

High-quality clear images are the basis for advanced vision tasks such as target detection and semantic segmentation. This paper proposes an image dehazing algorithm named mixed attention-based multi-scale feature calibration network, aiming at solving the problem of uneven haze distribution in low-quality fuzzy images acquired in foggy environments, which is difficult to remove effectively. Our algorithm adopts a U-shaped structure to extract multi-scale features and deep semantic information. In the encoding module, a mixed attention module is designed to assign different weights to each position in the feature map, focusing on the important information and regions where haze is difficult to be removed in the image. In the decoding module, a self-calibration recovery module is designed to fully integrate different levels of features, calibrate feature information, and restore spatial texture details. Finally, the multi-scale feature information is aggregated by the reconstruction module and accurately mapped into the solution space to obtain a clear image after haze removal. Extensive experiments show that our algorithm outperforms state-of-the-art image dehazing algorithms in various synthetic datasets and real hazy scenes in terms of qualitative and quantitative comparisons, and can effectively remove haze in different scenes and recover images with high quality.