A Multi-Scale Dehazing Network with Dark Channel Priors

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 13; p. 5980
• Main Authors: Yang, Guoliang, Yang, Hao, Yu, Shuaiying, Wang, Jixiang, Nie, Ziling
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.06.2023; MDPI
• Subjects: Algorithms; Benchmarking; Dark adaptation; dark channel priori; Deep learning; feature fusion; Feature maps; image dehazing; Learning; multi-scale; Neural networks; Neural Networks, Computer; Signal to noise ratio; Visual perception; dark channel priori; image dehazing; feature fusion; multi-scale; neural networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23135980

Image dehazing based on convolutional neural networks has achieved significant success; however, there are still some problems, such as incomplete dehazing, color deviation, and loss of detailed information. To address these issues, in this study, we propose a multi-scale dehazing network with dark channel priors (MSDN-DCP). First, we introduce a feature extraction module (FEM), which effectively enhances the ability of feature extraction and correlation through a two-branch residual structure. Second, a feature fusion module (FFM) is devised to combine multi-scale features adaptively at different stages. Finally, we propose a dark channel refinement module (DCRM) that implements the dark channel prior theory to guide the network in learning the features of the hazy region, ultimately refining the feature map that the network extracted. We conduct experiments using the Haze4K dataset, and the achieved results include a peak signal-to-noise ratio of 29.57 dB and a structural similarity of 98.1%. The experimental results show that the MSDN-DCP can achieve superior dehazing compared to other algorithms in terms of objective metrics and visual perception.