Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Open accessAbstract
We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (104 times higher than SRCNN [6]) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.
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SRCNN and VDSR are trained with bicubic blur kernel.
“Note that SRCNN and VDSR are trained with bicubic blur kernel, thus it is unfair to use their models to super-resolve the low-resolution image with Gaussian kernel.”
From Learning Deep CNN Denoiser Prior for Image Restoration (2017) · cites this paper as [35]
The FBPConvNet training clips the computed gradients to a fixed range to prevent the divergence of the cost function.
“We clip the computed gradients to a fixed range to prevent the divergence of the cost function [27], [46].”
From Deep Convolutional Neural Network for Inverse Problems in Imaging (2017) · cites this paper as [27]
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
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MethodInfluentialDelving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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Image Super-Resolution Using Deep Convolutional Networks
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Learning long-term dependencies with gradient descent is difficult
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“For example, RFL [18] has two methods, where the first one uses 91 images from Yang et al. [25] and the second one uses 291 images with the addition of 200 images from Berkeley Segmentation Dataset [16].”
MethodLearning a Deep Convolutional Network for Image Super-Resolution
Dong, C., Loy, C. C., He, K., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_13 · Cited by 5,827 · cited 6× · see citing works
“Ground Truth A+ [22] RFL [18] SelfEx [11] SRCNN [5] VDSR (Ours) (PSNR, SSIM) (27.”
MethodInfluentialImage Super-Resolution Via Sparse Representation
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MethodOn the difficulty of training recurrent neural networks
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“Adjustable Gradient Clipping Gradient clipping is a technique that is often used in training recurrent neural networks [17].”
MethodOn Single Image Scale-Up Using Sparse-Representations
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“Sparse coding [25, 26, 21, 22] methods use a learned compact dictionary based on sparse signal representation.”
MethodInfluentialLow-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding
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“Neighbor embedding [4, 15] methods interpolate the patch subspace.”
BackgroundMethodInfluentialMatConvNet: Convolutional Neural Networks for MATLAB
Vedaldi, A., & Lenc, K. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2807412 · Cited by 2,444 · cited 1× · see citing works
“We implement our model using the MatConvNet1 package [23].”
MethodSuper-resolution through neighbor embedding
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Improving resolution by image registration
Irani, M., & Peleg, B. (1991) · CVGIP Graphical Models and Image Processing · doi:10.1016/1049-9652(91)90045-l · Cited by 1,934 · cited 4× · see citing works
“SISR is widely used in computer vision applications ranging from security and surveillance imaging to medical imaging where more image details are required on demand.”
BackgroundInfluentialSuper-resolution from a single image
Glasner, D., Bagon, S., & Irani, M. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459271 · Cited by 1,889 · cited 3× · see citing works
“Early methods include interpolation such as bicubic interpolation and Lanczos resampling [7] more powerful methods utilizing statistical image priors [20, 13] or internal patch recurrence [9].”
BackgroundMethodFreeman, W. T., Pasztor, E. C., & Carmichael, O. T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026501619075 · Cited by 1,504 · cited 1× · see citing works
“We address the problem of generating a high-resolution (HR) image given a low-resolution (LR) image, commonly referred as single image super-resolution (SISR) [12], [8], [9].”
BackgroundA+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
Timofte, R., Smet, V. D., & Gool, L. V. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16817-3_8 · Cited by 1,470 · cited 10× · see citing works
“Sparse coding [25, 26, 21, 22] methods use a learned compact dictionary based on sparse signal representation.”
BackgroundMethodInfluentialAnchored Neighborhood Regression for Fast Example-Based Super-Resolution
Timofte, R., De, V., & Gool, L. V. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.241 · Cited by 1,373 · cited 5× · see citing works
“Sparse coding [25, 26, 21, 22] methods use a learned compact dictionary based on sparse signal representation.”
MethodInfluentialSingle-Image Super-Resolution Using Sparse Regression and Natural Image Prior
Kim, K. I., & Kwon, Y. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.25 · Cited by 993 · cited 1× · see citing works
“Early methods include interpolation such as bicubic interpolation and Lanczos resampling [7] more powerful methods utilizing statistical image priors [20, 13] or internal patch recurrence [9].”
MethodImage super-resolution using gradient profile prior
Sun, J., Xu, Z., & Shum, H. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587659 · Cited by 948 · cited 1× · see citing works
“Early methods include interpolation such as bicubic interpolation and Lanczos resampling [7] more powerful methods utilizing statistical image priors [20, 13] or internal patch recurrence [9].”
MethodMatConvNet - Convolutional Neural Networks for MATLAB
Vedaldi, A., & Lenc, K. (2014) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1412.4564 · Cited by 856 · cited 1× · see citing works
“We implement our model using the MatConvNet1 package [23].”
MethodFast and accurate image upscaling with super-resolution forests
Schulter, S., Leistner, C., & Bischof, H. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299003 · Cited by 633 · cited 8× · see citing works
“Ground Truth A+ [22] RFL [18] SelfEx [11] SRCNN [5] VDSR (Ours) (PSNR, SSIM) (27.”
MethodInfluentialFast Direct Super-Resolution by Simple Functions
Yang, C.-Y., & Yang, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.75 · Cited by 336 · cited 3× · see citing works
“[22] and Yang and Yang [24] for benchmark, is also employed.”
BackgroundA Comparison of Interpolation Methods for Sparse Data: Application to Wind and Concentration Fields
Goodin, W. R., McRae, G. J., & Seinfeld, J. H. (1979) · Journal of applied meteorology · doi:10.1175/1520-0450(1979)018 · Cited by 208 · see citing works
Super-resolution using neighbor embedding of back-projection residuals
Bevilacqua, M., Roumy, A., Guillemot, C. M., & Morel, M.-L. A. (2013) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2013.6622796 · Cited by 22 · cited 1× · see citing works
“Modelling image details is often used in super-resolution methods [21, 22, 15, 3] and we find that CNN-based methods can benefit from this domain-specific knowledge.”
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
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“Since pioneering work SRCNN [18] (for image SR), DnCNN [90] (for image denoising) and ARCNN [17] (for JPEG compression artifact reduction), a flurry of CNN-based models have been proposed to improve model representation ability by using larger and deeper neural network architecture designs, such as residual block [40,…”
BackgroundESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
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“3) Residual Learning: As a refinement of the original U-net, we add a skip connection [29] between input and”
BackgroundMethodInfluentialImage Super-Resolution via Deep Recursive Residual Network
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“…SISR method is compared with five state-of-the-art methods, including two CNN-based discriminative learning methods ( i.e ., SRCNN [21] and VDSR [35]), one statistical prediction model based discriminative learning method [45] which we refer to as SPMSR, one model based optimization method (…”
MethodPre-Trained Image Processing Transformer
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Second-Order Attention Network for Single Image Super-Resolution
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“However, most approaches [20, 26, 19, 24, 48] assume an ideal bicubic downsampling kernel, which is different from real degradations.”
BackgroundImage Super-Resolution Via Iterative Refinement
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“sion based and trained with an MSE loss [49], [52], [54], [55], [56].”
BackgroundDeep Back-Projection Networks for Super-Resolution
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Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
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“ods, numerous deep networks [10,11,45,19,23,30,67,65,7,41,40,29,26] have been proposed for SR to further improve the reconstruction quality.”
BackgroundMethodRecovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform
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BackgroundLearning Enriched Features for Real Image Restoration and Enhancement
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BackgroundSingle Image Super-Resolution via a Holistic Attention Network
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BackgroundConvolutional Neural Networks for Inverse Problems in Imaging: A Review
McCann, M. T., Jin, K. H., & Unser, M. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2739299 · Cited by 769 · see citing works
SRDiff: Single image super-resolution with diffusion probabilistic models
Li, H., Yang, Y., Chang, M., Chen, S., Feng, H., Xu, Z., Li, Q., & Chen, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.029 · Cited by 777 · see citing works
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793 · see citing works
“using only a single LR image [89]. Sub-pixel convolution layers can be introduced to improve ne details in the image, as reported in [91]. Residual learning and generative models are also employed in [93,94]. A generative model with a VGG-based49 perceptual loss function has been shown to signicantly improve quality a…”
MethodDeep learning for pixel-level image fusion: Recent advances and future prospects
Liu, Y., Chen, X., Wang, Z., Wang, Z. J., Ward, R. K., & Wang, X. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.10.007 · Cited by 739 · see citing works
Transformer for Single Image Super-Resolution
Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., & Zeng, T. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00061 · Cited by 755 · see citing works
“To address this issue, numerous deep neural networks have been proposed [10, 13, 18, 21, 22, 26, 39, 40, 45].”
BackgroundReverse Attention for Salient Object Detection
Chen, S., Tan, X., Wang, B., & Hu, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_15 · Cited by 734 · see citing works
MambaIR: A Simple Baseline for Image Restoration with State-Space Model
Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., & Xia, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72649-1_13 · Cited by 738 · see citing works
Deep Unfolding Network for Image Super-Resolution
Zhang, K., Gool, L. V., & Timofte, R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00328 · Cited by 689 · see citing works
Brief review of image denoising techniques
Fan, L., Zhang, F., Fan, H., & Zhang, C. (2019) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-019-0016-7 · Cited by 699 · see citing works
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution
Tian, Y., Zhang, Y., Fu, Y., & Xu, C. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00342 · Cited by 668 · see citing works
Residual Feature Aggregation Network for Image Super-Resolution
Liu, J., Zhang, W., Tang, Y., Tang, J., & Wu, G. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00243 · Cited by 652 · see citing works
“designed deeper VDSR [13] and DRCN [14] with 20 layers based on residual learning.”
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A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening
Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2794888 · Cited by 649 · see citing works
Medical Image Synthesis with Deep Convolutional Adversarial Networks
Nie, D., Trullo, R., Lian, J., Wang, L., Petitjean, C., Ruan, S., Wang, Q., & Shen, D. G. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2814538 · Cited by 626 · see citing works
Residual Feature Distillation Network for Lightweight Image Super-Resolution
Liu, J., Tang, J., & Wu, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-67070-2_2 · Cited by 623 · see citing works
Jo, Y., Oh, S. W., Kang, J., & Kim, S. J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00340 · Cited by 619 · see citing works
Toward Real-World Single Image Super-Resolution: A New Benchmark and a New Model
Cai, J., Zeng, H., Yong, H., Cao, Z., & Lei Zhang, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00318 · Cited by 611 · see citing works
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks
Yuan, Y., Liu, S., Zhang, J., Zhang, Y., Dong, C., & Lin, L. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00113 · Cited by 604 · see citing works
Deep-STORM: super-resolution single-molecule microscopy by deep learning
Nehme, E., Weiss, L. E., Michaeli, T., & Shechtman, Y. (2018) · Optica · doi:10.1364/optica.5.000458 · Cited by 589 · see citing works
Blind Super-Resolution With Iterative Kernel Correction
Gu, J., Lu, H., Zuo, W., & Dong, C. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00170 · Cited by 606 · see citing works
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Ignatov, A., Kobyshev, N., Timofte, R., & Vanhoey, K. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.355 · Cited by 608 · see citing works
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Chen, Y., Tai, Y. Y., Liu, X., Shen, C., & Yang, J. J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00264 · Cited by 600 · see citing works
The 2018 PIRM Challenge on Perceptual Image Super-Resolution
Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., & Zelnik‐Manor, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_21 · Cited by 609 · see citing works
Frame-Recurrent Video Super-Resolution
Sajjadi, M. S. M., Vemulapalli, R., & Brown, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00693 · Cited by 587 · see citing works
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT
Han, Y., & Ye, J. C. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2823768 · Cited by 590 · see citing works
Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods
Lucas, A. M., Iliadis, M., Molina, R., & Katsaggelos, A. K. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2760358 · Cited by 584 · see citing works
Detail-Revealing Deep Video Super-Resolution
Tao, X., Gao, H., Liao, R., Wang, J., & Jia, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.479 · Cited by 576 · see citing works
Recurrent Back-Projection Network for Video Super-Resolution
Haris, M., Shakhnarovich, G., & Ukita, N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00402 · Cited by 574 · see citing works
Compressed Sensing using Generative ModelsPreprint
Bora, A., Jalal, A., Price, E., & Dimakis, A. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.03208 · Cited by 577 · see citing works
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Menon, S., Damian, A., Hu, S., Ravi, N., & Rudin, C. D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00251 · Cited by 552 · see citing works
Edge-Enhanced GAN for Remote Sensing Image Superresolution
Jiang, K., Wang, Z., Yi, P., Wang, G., Lü, T., & Jiang, J. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2902431 · Cited by 547 · see citing works
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
Hu, X., Mu, H., Zhang, X., Wang, Z., Tan, T., & Sun, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00167 · Cited by 545 · see citing works
Learning Enriched Features for Fast Image Restoration and Enhancement
Zamir, S. W., Arora, A., Khan, S. H., Hayat, M., Khan, F. S., Yang, M., & Shao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3167175 · Cited by 561 · see citing works
“In contrast to directly producing a latent HR image, recent SR networks [59], [60], [61], [62] employ the residual learning framework [63] to learn the high-frequency image detail, which is later added to the input LR image to produce the final result.”
BackgroundMethodResultFully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal
Guan, S., Khan, A. A., Sikdar, S., & Chitnis, P. V. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2912935 · Cited by 538 · see citing works
Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution
Huang, H., He, R., Sun, Z., & Tan, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.187 · Cited by 537 · see citing works
Denoising Prior Driven Deep Neural Network for Image Restoration
Dong, W., Wang, P., Yin, W., Shi, G., Wu, F., & Lu, X. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2873610 · Cited by 521 · see citing works
LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement
Li, C., Guo, J., Porikli, F., & Pang, Y. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.01.010 · Cited by 510 · see citing works
Efficient Long-Range Attention Network for Image Super-Resolution
Zhang, X., Zeng, H., Guo, S., & Zhang, L. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19790-1_39 · Cited by 620 · see citing works
Non-Local Recurrent Network for Image RestorationPreprint
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Efficient Image Super-Resolution Using Pixel Attention
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SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network
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Sitzmann, V., Diamond, S., Peng, Y., Dun, X., Boyd, S., Heidrich, W., Heide, F., & Wetzstein, G. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201333 · Cited by 483 · see citing works
Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network
Wei, Y., Yuan, Q., Shen, H., & Zhang, L. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2736020 · Cited by 483 · see citing works
Eo, T., Jun, Y., Kim, T., Jang, J., Lee, H., & Hwang, D. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27201 · Cited by 455 · see citing works
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One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
Wang, T.-C., Mallya, A., & Liu, M.-Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00991 · Cited by 467 · see citing works
“…[48], such as unconditional image synthesis [21, 23, 32, 33, 34, 49, 61], image translation [8, 12, 27, 29, 46, 47, 58, 67, 73, 84, 102, 103], text-to-image translation [62, 90, 95], image processing [17,18,28,36,39,41,42,44,72,78,89,94], and video synthesis [2,10,35,45,50,60,63,69,82,83,101].”
BackgroundFrom Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur
Gong, D., Yang, J., Liu, L., Zhang, Y., Reid, I. D., Shen, C., van den Hengel, A., & Shi, Q. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.405 · Cited by 455 · see citing works
Kim, J., Kim, M., Kang, H.-W., & Lee, K.-H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10830 · Cited by 503 · see citing works
Self-citationA Deep Journey into Super-resolution
Anwar, S., Khan, S. H., & Barnes, N. M. (2020) · ACM Computing Surveys · doi:10.1145/3390462 · Cited by 483 · see citing works
- Authors
- Kim, J., Lee, J. K., & Lee, K. M.
- Affiliations
- Seoul National University (KR)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1646-1654
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2242218935
- Cited by
- 7,870
- References
- 26
- Citations by year
- 2026: 395 · 2025: 814 · 2024: 967 · 2023: 1009 · 2022: 966 · 2021: 1065 · 2020: 996 · 2019: 889 · 2018: 523 · 2017: 220 · 2016: 26
- FWCI
- 191.69
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- single image super-resolution, network depth, residual learning, gradient clipping, training convergence
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