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Super-resolution is an important task in the image and video processing domain. In mixed-resolution multi-view scenarios, neighboring high-resolution reference perspectives can be used to increase the image quality of a given low-resolution target view. By using corresponding depth information, the required high-frequency part can be projected from a reference view onto the image plane of the target...
Increasing spatial resolution and thus improving the image quality is a key issue in the mixed-resolution multiview image and video processing domain. Given adjacent camera perspectives with various spatial resolutions and their corresponding depth information, the high-frequency part of a high-resolution view can be used for increasing the image quality of a neighboring low-resolution camera perspective...
In mixed-resolution multiview setups, spatially neighboring high-resolution views can be used to increase the image quality of an intermediate low-resolution camera perspective. By utilizing corresponding depth data, high-frequency information can be projected from the reference perspectives onto the image plane of the low-resolution view, creating the desired super-resolved image. In this context,...
In mixed-resolution multiview setups, a scene is captured from various viewpoints with cameras having different spatial resolutions. Compared to full-resolution systems, mixed-resolution setups allow for savings with respect to data transmission, storage, and costs. However, for applications like free viewpoint television, high-quality images are required for all available camera perspectives. Therefore,...
Increasing the image sharpness of low-resolution views is a key issue in the multiview image and video processing domain. Thereby, a low-resolution view gets refined by high-frequency content that can either be obtained from temporally or spatially adjacent highresolution reference images. We propose a refined super-resolution algorithm for multiview images that is robust to the usage of temporally...
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