A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution

By Radu Timofte, Vincent De Smet, Luc Van Gool. 2014

1,508 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (2021, background): “Single image super-resolution (SISR), which aims to reconstruct the natural and sharp detailed high-resolution (HR) counterpart x from a low-resolution (LR) image y [9, 44], has recently drawn significant attention due to its high practical value.”
The Perception-Distortion Tradeoff (2017, methodology): “The evaluated algorithms include: A+ [37], SRCNN [7], SelfEx [12], FSRCNN [8], VDSR [17], Johnson et al.”
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (2016, methodology): “We evaluate the run time of other methods [2, 51, 39] from the Matlab codes provided by [40] and [31].”
Image Super-Resolution Using Deep Convolutional Networks (2014, methodology): “This pipeline is shared by most external example-based methods, which pay particular attention to learning and optimizing the dictionaries [2], [51], [52] or building efficient mapping functions [24], [42], [43], [49].”
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].”
Accurate Image Super-Resolution Using Very Deep Convolutional Networks (2015, methodology): “Sparse coding [25, 26, 21, 22] methods use a learned compact dictionary based on sparse signal representation.”
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (2017, methodology): “We observe that methods using the bicubic upsampling for pre-processing generate results with noticeable artifacts [7, 17, 26, 30, 33].”
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (2016, background): “It should be noted that, prior to the residual network [22], the strategy of predicting the residual image has already been adopted in some low-level vision problems such as single image super-resolution [31] and color image demosaicking [32].”
Learning a No-Reference Quality Metric for Single-Image Super-Resolution (2016, methodology): “Numerous single-image SR algorithms have been recently proposed for generic images that exploit priors based on edges [1], gradients [2, 3], neighboring interpolation [4, 5], regression [6], and patches [7, 8, 9, 10, 11, 12, 13, 14, 15].”
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution (2017, methodology): “Furthermore, approaches including kernel regression [42], random forests[31], and anchored neighborhood regression [35], have been proposed for SR to improve accuracy and speed, all related to methods presented in SD literature [5, 13, 32].”
Enhancing Image Quality via Style Transfer for Single Image Super-Resolution (2018, methodology): “The comparison methods include A+ [7], SRCNN [8], ESPCN [17], SRResNet [1], and SRGAN [1].”
Residual Dense Network for Image Restoration (2018, background): “Recently, deep convolutional neural network (CNN) has shown its excellent performance in different image restoration tasks, such as image super-resolution (SR) [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], image denoising (DN) [15], [16], [17], [18], [19], [20], and image compression r”
HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images (2018, methodology): “[1] introduced A+ [32] from super-resolution to further improve Arad’s method.”