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https://www.selleckchem.com/products/trc051384.html Finally, comprehensive experiments over seven benchmark datasets speak to the effectiveness as well as efficiency of the proposed algorithm.This paper presents a new approach for synthesizing a street-view panorama given a satellite image as if captured from the geographical location at the center of the satellite image. Existing works approach this as an image generation problem, adopting generative adversarial networks to implicitly learn the cross-view transformations, but ignore the geometric constraints. In this paper, we make the geometric correspondences between the satellite and street-view images explicit to facilitate the transfer of information between domains. Specifically, we observe that when a 3D point is visible in both views, and the height of the point relative to the camera is known, there is a deterministic mapping between the projected points in the images. Motivated by this, we develop a novel satellite to street-view projection (S2SP) module which learns the height map and projects the satellite image to the ground-level viewpoint, explicitly connecting corresponding pixels. With these projected satellite images as input, we next employ a generator to synthesize realistic street-view panoramas that are geometrically consistent with the satellite images. Our S2SP module is differentiable and the whole framework is trained in an end-to-end manner. Extensive experimental results demonstrate that our method generates more accurate and consistent images than existing approaches.In the above article [1], the article title was incorrect. The correct article title is "Deep Back-Projection Networks for Single Image Super-Resolution."This study presents a highly miniaturized, handheld probe developed for rapid assessment of soft tissue using optical coherencetomography (OCT). OCT is a non-invasive optical technology capable of visualizing the sub-surface structural changes that occur in soft tissue dis

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