Edge-directed interpolation

By J. Allebach, Ping Wah Wong. 1996

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016, methodology): “Interpolation methods that put particular focus on edge-preservation have been proposed in for example Allebach and Wong [1] or Li et al.”
Enhanced Deep Residual Networks for Single Image Super-Resolution (2017, methodology): “To solve the super-resolution problem, early approaches use interpolation techniques based on sampling theory [1, 15, 34].”
Learning Enriched Features for Fast Image Restoration and Enhancement (2022, background): “Prior to the deep-learning era, numerous super-resolution (SR) algorithms have been proposed based on the sampling theory [43], [44], edge-guided interpolation [45], [46], natural image priors [47], [48], patch-exemplars [49], [50] and sparse representations [51], [52].”
Super resolution using edge prior and single image detail synthesis (2010, background): “, [1, 14, 4, 27]) have their foundations in sampling theory and try to interpolate the HR image from the LR input.”
Super-Resolution With Sparse Mixing Estimators (2010, background): “Many adaptive interpolations have been, thus, developed with edge detectors [2], [32], [51] and by finding the direction of regularity with gradient operators [1], [6], [7], [13], [16], [33], [35], [53], [59], [60], [63].”
New edge-directed interpolation (2001, methodology): “Edge-directed interpolation techniques [9], [10] employ a source model”
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification (2001, background): “More sophisticated schemes may locate the edges first with local filters prior to interpolation so as to avoid the blurring artifacts [2], [31].”
Image hallucination with primal sketch priors (2003, methodology): “Edge-based interpolation methods [2, 15] have also been proposed.”
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss (2018, methodology): “One classic SR method is the interpolation-based approach, such as cubicbased [16], edge-directed [1, 26] and wavelet-based [36] methods.”
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network (2018, methodology): “Early approaches on SISR used sampling theory based interpolation techniques [2,29,50] to recover the lost details.”
Learning Enriched Features for Real Image Restoration and Enhancement (2020, methodology): “Prior to the deep-learning era, numerous SR algorithms have been proposed based on the sampling theory [55,53], edge-guided interpolation [4,122], natural image priors [58,110], patch-exemplars [15,33] and sparse representations [114,113].”
SRFeat: Single Image Super-Resolution with Feature Discrimination (2018, methodology): “To overcome this limitation, and to enhance edges, edge preserving interpolation [18, 19] and edge prior-based approaches [20–22] were proposed.”