Abstract
We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing. Compare to the previously proposed dehazing network, the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection, and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions. A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR, SSIM, and subjective visual quality. In addition, it achieved a good performance in speed by using EfficientNet B0 as a feature extractor. We find that only using high-level semantic features can not effectively obtain all the information in the image. The FPN structure used in this paper can effectively integrate the high-level semantics and the low-level semantics, and can better take into account the global and local features. The five feature maps with different sizes are not simply weighted and fused. In order to keep all their information, we put them all together and get the final features through decode layers. At the same time, we have done a comparative experiment between ResNet with FPN and EfficientNet with BiFPN. It is proved that EfficientNet with BiFPN can obtain image features more efficiently. Therefore, EfficientNet with BiFPN is chosen as our network feature extraction.
Highlights
Due to the presence of turbid media in the atmosphere, the visibility of the images captured by the camera will be greatly affected, such as the loss of contrast and saturation, and the overall brightness of the image will be dark
We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing
A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR, SSIM, and subjective visual quality
Summary
Due to the presence of turbid media in the atmosphere (e.g., dust, mist, smoke, and haze), the visibility of the images captured by the camera will be greatly affected, such as the loss of contrast and saturation, and the overall brightness of the image will be dark. This lack of clarity has an adverse effect on the postprocessing of the computer vision system, which interferes with the performance of the computer vision system. The major difference between these methods is that the former is based on statistical prior information for dehazing and while the latter is self-adaptive dehazing through learning
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
