Learning a Deep Convolutional Network for Image Super-Resolution

By Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. 2014

5,733 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Rethinking the Inception Architecture for Computer Vision (2015, background): “…[16] winning entry by Krizhevsky et al [9], their network “AlexNet” has been successfully applied to a larger variety of computer vision tasks, for example to object-detection [5], segmentation [12], human pose estimation [22], video classiﬁcation [8], object tracking [23], and superresolution [3].”
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (2016, methodology): “Since the 2012 ImageNet competition [11] winning entry by Krizhevsky et al [8], their network “AlexNet” has been successfully applied to a larger variety of computer vision tasks, for example to object-detection [4], segmentation [10], human pose estimation [17], video classification [7], object tra”
Learning to generate chairs with convolutional neural networks (2014, background): “Our work is also loosely related to applications of CNNs to non-discriminative tasks, such as super-resolution [6] or inferring depth from a single image [8].”
Instance-Sensitive Fully Convolutional Networks (2016, background): “These fully convolutional models are naturally applicable for image restoration problems, such as denoising [16], super-resolution [17], and others [18], where each output pixel is a real-number regressor of intensity values.”
CogView: Mastering Text-to-Image Generation via Transformers (2021, background): “Previous works [13] about super-resolution, or image restoration, usually deal with images already in high resolution, mapping the blurred local textures to clear ones.”
Image Super-Resolution via Iterative Refinement (2021, neutral): “sion based and trained with an MSE loss [49], [52], [54], [55], [56].”
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (2021, methodology): “Early non-blind SISR methods were mainly designed for bicubic degradations [10].”
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016, methodology): “Table 1: Comparison of methods: bicubic, SRCNN [7], SelfExSR [24], DRCN [27], ESPCN [40] on Set5, Set14, BSD100 benchmark data.”
Perceptual Losses for Real-Time Style Transfer and Super-Resolution (2016, positive): “Ground Truth Bicubic SRCNN [13] Perceptual loss”
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (2015, background): “Other applications where pixel wise predictions are made using deep networks are image super-resolution [48] and depth map prediction from a single image [49].”
Multimodal Unsupervised Image-to-Image Translation (2018, background): “Many problems in computer vision aim at translating images from one domain to another, including super-resolution [1], colorization [2], inpainting [3], attribute transfer [4], and style transfer [5].”
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search (2019, background): “Most of the recent works on SISR have shifted their approaches to deep learning, and they have surpassed other SISR algorithms with big margins [ Dong et al. , 2014; Kim et al. , 2016a; He et al. , 2016; Ahn et al. , 2018].”
Learning Implicit Fields for Generative Shape Modeling (2018, background): “, [11, 25], which are trained with low- and high-resolution image pairs.”
The Perception-Distortion Tradeoff (2017, methodology): “The evaluated algorithms include: A+ [42], SRCNN [9], SelfEx [13], VDSR [18], Johnson et al .”
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (2016, methodology): “We first evaluate the power of the sub-pixel convolution layer by comparing against SRCNN’s standard 9-1-5 model [6].”
Deep Image Prior (2017, background): “Here we focus on the case where the distribution is conditioned on a corrupted observation x0 to solve inverse problems such as denoising [5] and super-resolution [7].”
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation (2016, background): “, depth estimation [15, 13, 33], image restoration [14], image super-resolution [12].”
Image Super-Resolution Using Deep Convolutional Networks (2014, background): “Index Terms—Super-resolution, deep convolutional neural networks, sparse coding F”