Multi-scale dictionary for single image super-resolution
By Kaibing Zhang, Xinbo Gao, Dacheng Tao, Xuelong Li. 2012
132 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Ling Shao
- Thomas Baltzer Moeslund
- Kamal Nasrollahi
- Xuelong Li
- Rajagopalan Ambasamudram Narayanan
- Thekke Madam Nimisha
- Subeesh Vasu
- Yan Liu
In Media Technology
- Thomas Baltzer Moeslund
- Kamal Nasrollahi
- Rajagopalan Ambasamudram Narayanan
- Thekke Madam Nimisha
- Subeesh Vasu
- Xuelong Li
- Mostafa El-Khamy
- Ling Shao
Citations in the corpus
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (2016, neutral): “Recent popular SISR methods can be classified into edge-based [35], image statisticsbased [9, 18, 46, 12] and patch-based [2, 43, 52, 13, 54, 40, 5] methods.”
- EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (2016, neutral): “They further include dictionary-based methods [33, 40, 52, 59, 61, 64] that learn a sparse representation of image patches as a combination of dictionary atoms, as well as neural network-based approaches [4, 8, 9, 24, 25, 26, 47, 48, 62] which apply convolutional neural networks (CNNs) to the task…”
- Image Super Resolution Based on Fusing Multiple Convolution Neural Networks (2017, methodology): “The typical methods include neighbor embedding [7], Markov network [22], kernel regression [4], random forest [26], sparse coding [25][9][10][12], and convolution neural networks.”
- Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network (2018, methodology): “[30] tried to capture the patch redundancy across different scales using a multi-scale dictionary.”
- Super-resolution: a comprehensive survey (2014, methodology): “Structural Similarity Measures [220], [270], [275], [293], [355], [366], [420], [428], [439], [449], [456], [485], [493], [495], [507], [520], [522], [524], [527], [552], [554], [555], [557], [558], [561], [563], [568], [569], [570], [571], [572], [579], [586], [590], [595], [601], [608], [612]”
- From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms (2014, background): “As an emerging machine learning technique, sparse representations have become a trend and are used for restoration problems [11], [40], [41].”