Abstract
Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal. The proposed network, which is constructed based on dense blocks and attention blocks, contains an encoder-decoder architecture, which enables it to directly learn the mapping between the input images and the corresponding haze-free image, without being dependent on the traditional atmospheric scattering model (ASM). To better handle non-uniform hazy remote sensing images, we propose to combine a haze density prior with deep learning, where an initial haze density map (HDM) is firstly extracted from the original hazy image, and is subsequently utilized as the input of the network, together with the original hazy image. Meanwhile, a large-scale hazy remote sensing dataset is created for training and testing of the proposed method, which contains both uniform and non-uniform, synthetic and real hazy remote sensing images. Experimental results on the created dataset illustrate that the developed dehazing method obtains significant progresses over the state-of-the-art methods.
Highlights
Remote sensing imageries are being increasingly utilized in the fields of numerous applications with the advances of remote sensing technology, such as agriculture and weather studies [1], land cover monitoring [2,3,4], and so on
The deep learning based All-in-one Dehazing Network (AOD-Net) and densely connected pyramid dehazing network (DCPDN) are clearly sensitive to the non-uniform haze distribution, and retain obvious vestiges of non-uniform haze in their dehazed results, indicating that detecting and removing non-uniform haze from a single remote sensing image may be a rather difficult task for these deep learning-based methods, if there is not any additional prior information
Fast visibility restoration (FVR) retains much of the haze in the result images, and introduces obvious color distortions and artifacts
Summary
Remote sensing imageries are being increasingly utilized in the fields of numerous applications with the advances of remote sensing technology, such as agriculture and weather studies [1], land cover monitoring [2,3,4], and so on. For the image dehazing problem, earlier works utilized multiple images of the same scenery [5,6,7,8]. In [9], Xu et al presented a solution based on contrast limited adaptive histogram equalization to remove haze from single-color images. Narasimhan et al [10] proposed a physical-based model to describe the appearances of scenery under uniform bad weather conditions and utilizes a quick algorithm to recover the scene contrast. These enhancement methods do not take the reasons for the image degradation into account, leading to common over-estimation, under-estimation, and color shift problems
Sign-in/Register to view highlights & summary 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.
