Single Image Dehazing via NIN-DehazeNet

• Main Authors: Kangle Yuan, Jianguo Wei, Wenhuan Lu, Naixue Xiong
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Single image dehazing; manual features; deep learning; NIN-DehazeNet; Network-in-Network; multi-scale convolutional neural networks,atmospheric scattering model
• Online Access: https://ieeexplore.ieee.org/document/8930499/

Single image dehazing has always been a challenging problem in the field of computer vision. Traditional image defogging methods use manual features. With the development of artificial intelligence, the defogging method based on deep learning has developed rapidly. In this paper, we propose a novel image defogging approach called NIN-DehazeNet for single image. This method estimates the transmission map by NIN-DehazeNet combining Network-in-Network with MSCNN(Single Image Dehazing via Multi-Scale Convolutional Neural Networks). In the test stage, we estimate the transmission map of the input hazy image based on the trained model, and then generate the dehazed image using the estimated atmospheric light and computed transmission map. Extensive experiments have shown that the proposed algorithm overperformance traditional methods.