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Hera mission is the European Space Agency's contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation. • The Image Processing of Hera is investigated using Convolutional Neural Networks. • The centroid regression of both Didymos and Dimorphos is solved. • The range and the Sun phase angle are estimated using the pinhole camera model. • The subsolar point position on the surface of Didymos is estimated. [ABSTRACT FROM AUTHOR]

Abstract: Automated image quality assessment is highly desirable to evaluate the performance of various image fusion algorithms for night vision applications. In this paper we propose a perceptual quality evaluation method for image fusion which is based on human visual system (HVS) models. Our method assesses the image quality of a fused image using the following steps. First, the source and fused images are filtered by a contrast sensitivity function (CSF) after which a local contrast map is computed for each image. Second, a contrast preservation map is generated to describe the relationship between the fused image and each source image. Finally, the preservation maps are weighted by a saliency map to obtain an overall quality map. The mean of the quality map indicates the quality of the fused image. Experimental results compare the predictions made by our algorithm with human perceptual evaluations for several different parameter settings in our algorithm. The most popular existing algorithms are also evaluated. For some specific parameter settings, we find our algorithm provides better predictions, which are more closely matched to human perceptual evaluations, than the existing algorithms. The evaluations focus on the night vision application, but the algorithm we propose is applicable to other applications also. [Copyright &y& Elsevier]