An edge-guided image interpolation algorithm via directional filtering and data fusion

By Lei Zhang, Xiaolin Wu. IEEE Transactions on Image Processing, 2006

1,039 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/be1201b2bf12c63cb01b5f4048536e9320a8b353

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Correction to: Single Image Super-Resolution via a Holistic Attention Network (2020, methodology): “We compare the proposed method with nine state-of-the-art super-resolution methods: SPMSR [25], SRCNN [3], FSRCNN [4], VDSR [15], IRCNN [37], SRMD [39], RDN [41], RCAN [40],SRFBN [21] and SAN [2].”
Robust Image Completion via Deep Feature Transformations (2019, neutral): “In general, existing work on image inpainting can be categorized into a number of classes, including interpolation methods [31], [32], exemplar based methods [33], partial differential equation (PDE) based methods [34], variational methods [35], and learning-based methods [25], [36].”
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.”
Feedback Network for Image Super-Resolution (2019, methodology): “To address this problem, numerous image SR methods have been proposed, including interpolation-based methods[45], reconstruction-based methods[42], and learning-based methods [33, 26, 34, 15, 29, 6, 18].”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “All these methods require considerable A u t h o r ' s p e r s o n a l”
Residual Feature Aggregation Network for Image Super-Resolution (2020, methodology): “Many image SR methods have been proposed to tackle this inverse problem, including early interpolationbased [37], reconstruction-based [34], and recent learning based methods [27, 28, 22, 4, 12, 13, 36, 3].”
Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network (2018, methodology): “The spatial-based methods include interpolation methods [15], [16], exemplar-”
Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity (2010, methodology): “The PLE interpolation zooming is compared with linear inter polators [8], [37], [63], [52] as well as recent super-resolution algorithms “NEDI” (new edge directed int erpolation) [39], “DFDF” (directional filtering and data fusion) [68], “KR” (kernel regression) [60], “ECM” (expectation conditional ”
Residual Dense Network for Image Super-Resolution (2018, methodology): “To tackle this inverse problem, plenty of image SR algorithms have been proposed, including interpolationbased [36], reconstruction-based [34], and learning-based methods [27, 28, 19, 2, 20, 8, 10, 30].”
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 [45], [46], edge-guided interpolation [47], [48], natu-ral image priors [49], [50], patch-exemplars [51], [52] and sparse representations [53], [54].”
Multilevel and Multiscale Network for Single-Image Super-Resolution (2019, neutral): “Traditional methods include interpolation-based methods [6],”
RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution (2019, background): “Thanks to the strong learning capability, Convolutional Neural Networks (CNNs) have demonstrated superior performance [10, 24, 42] to the conventional example-based [40] and interpolation-based [41] algorithms.”
Image Super-Resolution With Sparse Neighbor Embedding (2012, neutral): “into three categories: interpolation-based methods [2]–[4], mul-”
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].”