Example-Based Super-Resolution

By William T. Freeman, Thouis Raymond Jones, Egon C. Pasztor. 2002

2,657 citations recorded by the source corpus.

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): “Early work was presented by Freeman et al. [17, 16].”
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (2015, methodology): “The non-parametric models often do matching from a database of existing images, often matching patches of images, and have been used in texture synthesis (Efros et al., 1999), super-resolution (Freeman et al., 2002) and in-painting (Hays & Efros, 2007).”
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks (2015, background): “The former copy patches from training images to perform, for example, texture synthesis [7] or super-resolution [9].”
Perceptual Losses for Real-Time Style Transfer and Super-Resolution (2016, methodology): “They group super-resolution techniques into prediction-based methods (bilinear, bicubic, Lanczos, [24]), edge-based methods [25,26], statistical methods [27,28,29], patch-based methods [25,30,31,32,33,34,35,36], and sparse dictionary methods [37,38].”
Generative Image Modeling Using Style and Structure Adversarial Networks (2016, methodology): “Non-parametric approaches perform matching of an image or patch with the database for tasks such as texture synthesis [20] or super-resolution [21].”
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation (2018, methodology): “Traditionally, image super-resolution has used dictionary-style methods [7, 48], matching patches of images to higher-resolution counterparts.”
Image Super-Resolution Using Deep Convolutional Networks (2014, methodology): “The sparse-coding-based method [49], [50] is one of the representative external example-based SR methods.”
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].”
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (2017, methodology): “Numerous SR meth-ods learn the LR-HR mapping with image pairs collected from external databases using supervised learning algorithms, such as nearest neighbor [10], manifold embedding [2, 5], kernel ridge regression [19], and sparse representation [37, 38, 39].”
Pixel Recursive Super Resolution (2017, background): “Much work has been done on algorithms that search a database of patches and combine them to create plausible high frequency details in zoomed images [7, 13].”
Super-Resolution with Deep Convolutional Sufficient Statistics (2015, background): “The high-resolution instance is produced by copying small patches from the most similar examples found in the training set (Freeman et al. (2002)).”
Deep Learning for Image Super-Resolution: A Survey (2019, methodology): “In literature, a variety of classical SR methods have been proposed, including prediction-based methods [12], [13], [14], edge-based methods [15], [16], statistical methods [17], [18], patch-based methods [15], [19], [20], [21] and sparse representation methods [22], [23], etc.”
Image super-resolution using gradient profile prior (2008, methodology): “The learning based methods [10, 9 , 26, 5, 28, 2, 7, 20, 31] “hallucinate” high frequency details from a training set of HR/LR image pairs.”
Image super-resolution: The techniques, applications, and future (2016, methodology): “In the early stage, patch or feature-based approaches were used to learn the generic relationship between the LR and HR image details [12,160,161].”
Image super-resolution as sparse representation of raw image patches (2008, methodology): “The one-pass algorithm [ 13 ] is shown to work almost as well as the use of a full MRF model [12].”
Learning a Deep Convolutional Network for Image Super-Resolution (2014, background): “[8], the dictionaries are directly presented as low/high-resolution patch pairs, and the nearest neighbour (NN) of the input patch is found in the low-resolution space, with its corresponding high-resolution patch used for reconstruction.”
Image upsampling via imposed edge statistics (2007, methodology): “Upsampling methods that are based on a non-parametric model, such as [Freeman et al. 2002], may suffer from a shortage of structures.”
Image Super-Resolution via Dual-State Recurrent Networks (2018, background): “It demonstrated superior performance to many other example-based learning paradigms, such as nearest neighbor [13], sparse representation [44, 43], neighborhood embedding [5, 37], random forest [32], etc.”
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (2010, background): “The high-pass filtering is often used in low-level statistical learning tasks to enhance the meaningful features [50].”