DEEP LEARNING MODEL FOR HAZE REMOVAL FROM REMOTE SENSING IMAGES
Main Article Content
Abstract
Satellite image haze removal techniques are extensively used in several outdoor applications. Lack of sufficient knowledge that is required to restore hazy satellite images, the existing techniques usually use various attributes and assign constant values to these attributes. Unsuitable assignment to these attributes does not provide desired dehazing results. The primary objective of this review paper is to provide a structured outline of some well-known haze removal techniques. Thus, to overcome these drawbacks, the proposed research is implemented with the advanced Deep learning convolution neural network (DLCNN) model. The network is trained and tested with the Laplacian and Gaussian pyramid-based features. These features are used to modify the multiple exposure Fusion (MEF) properties respectively; thus, accurate enhancement is possible. The proposed DLCNN-MEF technique gives better results when compared with state of art approaches technique with respect to the various parameters including PSNR, SSIM and MSE values for comparing the results
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
