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• W3197624229 abstract "We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and high-fidelity results. Our proposed method generalizes the standard patch-based feature matching with spatial alignment operations. We further explore the dual-camera super-resolution that is one promising application of RefSR, and build a dataset that consists of 146 image pairs from the main and telephoto cameras in a smartphone. To bridge the domain gaps between real-world images and the training images, we propose a self-supervised domain adaptation strategy for real-world images. Extensive experiments on our dataset and a public benchmark demonstrate clear improvement achieved by our method over state of the art in both quantitative evaluation and visual comparisons. Our code and data are available at https://tengfei-wang.github.io/Dual-Camera-SR/index.html." @default.
• W3197624229 created "2021-09-13" @default.
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• W3197624229 date "2021-10-01" @default.
• W3197624229 modified "2023-10-05" @default.
• W3197624229 title "Dual-Camera Super-Resolution with Aligned Attention Modules" @default.
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• W3197624229 doi "https://doi.org/10.1109/iccv48922.2021.00201" @default.
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