![]() ![]() IEEE transactions on visualization and computer graphics, 14(1), 97-108. Real-time adaptive radiometric compensation. International Journal of Remote Sensing, 32(23), 8685-8697. Haze detection, perfection and removal for high spatial resolution satellite imagery. In Proceedings of the IEEE international conference on computer vision (pp. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. Simultaneous Detection and Removal of High Altitude Clouds from an Image. Thin cloud removal from single satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 153, 137-150. Thin cloud removal with residual symmetrical concatenation network. In 2016-10th International Conference on Signal Processing and Communication Systems (ICSPCS) (pp. Cloud removal for single images based on dual tree complex wavelet transform. ![]() In 2015-6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO) (pp. Adaptive flight control for quadrotor UAV in the presence of external disturbances. Bouadi, H., Aoudjif, A., & Guenifi, M.Use of principal components of UAV-acquired narrow-band multispectral imagery to map the diverse low stature vegetation fAPAR. International Archives of the Photogrammetry, Remote Sensing Spatial Information Sciences, 41. A FAST APPROACH FOR STITCHING OF AERIAL IMAGES. Overall, the authors introduced a new frontier in generating cloud-free aerial images and added a valuable contribution to the array of cloud removal algorithms. Also, the proposed algorithm outperformed some of existing cloud removal algorithms by producing a better quality output when tested on the too-cloudy satellite images. This technique showed flexibility in performing the given task with satisfactory results, which is a gauge based on No-Reference Image Quality Metric, specifically the Perception-based Image Quality Evaluator (PIQE). The proposed cloud removal technique using the generative adversarial network with simple image enhancement (SIE-GAN) is a useful tool in removing cloud formations, most notably in images acquired using Unmanned Aerial Vehicle System (UAVs). Hence, an effective cloud removal algorithm is a significant factor for this kind of problem and other related applications. It degrades the visual quality of images leading to the loss of information for ground scenes. The atmospheric condition of the presence of clouds is one of the biggest problems in most aerial imaging systems. ![]()
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