Landmark Image Super-Resolution by Retrieving Web Images

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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016, methodology): “Photo-realistic image super-resolution techniques including [44, 60, 57] have been focusing on minimizing the perceptual differences by using detail synthesis, a multi-scale dictionary or a structure aware loss function.”
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (2016, background): “In recent years, popular approaches include exemplar-based models that either exploit recurrent patches of different scales within a single image [13, 17, 22, 56] or learn mappings between low and high resolution pairs of image patches in external databases [3, 5, 14, 27, 51, 58, 63].”
Image Super-Resolution by Neural Texture Transfer (2019, methodology): “, Landmark [39] and the recently proposed CrossNet [41], which outperforms previous RefSR methods.”
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network (2018, methodology): “dundancy across dierent scales using a multi-scale dictionary. HR images from the web with similar contents were used with-in a structure-aware matching criterion to super-resolve landmark images in [48]. The class of neighbor embedding approaches [8,3,17,44,45] aim to nd similar looking LR training”
Learning Texture Transformer Network for Image Super-Resolution (2020, methodology): “Landmark [36] aligned the Ref image to the LR image through a global registration to solve an”