| NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results | 0 | 0.34 | 2022 |
| NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results. | 0 | 0.34 | 2022 |
| Towards Interpretable Video Super-Resolution via Alternating Optimization. | 0 | 0.34 | 2022 |
| Plug-and-Play Image Restoration With Deep Denoiser Prior | 11 | 0.50 | 2022 |
| Reference-Based Image Super-Resolution with Deformable Attention Transformer. | 0 | 0.34 | 2022 |
| Flow-based Kernel Prior with Application to Blind Super-Resolution | 0 | 0.34 | 2021 |
| CNN-based Super Resolution for Video Coding Using Decoded Information | 0 | 0.34 | 2021 |
| Designing a Practical Degradation Model for Deep Blind Image Super-Resolution. | 0 | 0.34 | 2021 |
| Convolutional Neural Network Based In-Loop Filter For VVC Intra Coding. | 0 | 0.34 | 2021 |
| The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures | 0 | 0.34 | 2021 |
| Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution. | 0 | 0.34 | 2021 |
| SwinIR: Image Restoration Using Swin Transformer | 2 | 0.38 | 2021 |
| SwinIR: Image Restoration Using Swin Transformer. | 0 | 0.34 | 2021 |
| Hierarchical Conditional Flow - A Unified Framework for Image Super-Resolution and Image Rescaling. | 0 | 0.34 | 2021 |
| Neural Blind Deconvolution Using Deep Priors | 2 | 0.36 | 2020 |
| AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results | 1 | 0.41 | 2020 |
| NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results | 1 | 0.36 | 2020 |
| Deep Unfolding Network For Image Super-Resolution | 7 | 0.42 | 2020 |
| NTIRE 2019 Challenge on Real Image Denoising: Methods and Results | 5 | 0.39 | 2019 |
| Extreme Learning Machine With Affine Transformation Inputs in an Activation Function. | 3 | 0.39 | 2019 |
| Deep Plug-And-Play Super-Resolution For Arbitrary Blur Kernels | 7 | 0.41 | 2019 |
| End-to-End Blind Image Quality Assessment Using Deep Neural Networks. | 53 | 1.19 | 2018 |
| NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. | 77 | 1.71 | 2018 |
| Toward Convolutional Blind Denoising Of Real Photographs | 30 | 1.12 | 2018 |
| Multi-level Wavelet-CNN for Image Restoration | 18 | 0.56 | 2018 |
| Single Image Super-Resolution with a Parameter Economic Residual-Like Convolutional Neural Network. | 4 | 0.41 | 2017 |
| Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. | 269 | 6.36 | 2017 |
| FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising. | 72 | 1.76 | 2017 |
| Class Relatedness Oriented-Discriminative Dictionary Learning for Multiclass Image Classification | 5 | 0.39 | 2016 |
| Extreme learning machine and adaptive sparse representation for image classification. | 61 | 1.36 | 2016 |
| The Effect of Quantization Setting for Image Denoising Methods: An Empirical Study. | 0 | 0.34 | 2016 |
| Joint Learning of Multiple Regressors for Single Image Super-Resolution | 16 | 0.53 | 2016 |
| Outlier-robust extreme learning machine for regression problems. | 0 | 0.34 | 2015 |
| Symmetric Intra Block Copy In Video Coding | 3 | 0.43 | 2015 |
| Revisiting Single Image Super-Resolution Under Internet Environment: Blur Kernels and Reconstruction Algorithms. | 5 | 0.41 | 2015 |
| Robustness of full implication algorithms based on interval-valued fuzzy inference | 11 | 0.57 | 2015 |
| Advanced Motion Information Prediction And Inheritance In 3d-Hevc | 0 | 0.34 | 2015 |
| A hybrid approach combining extreme learning machine and sparse representation for image classification | 23 | 0.77 | 2014 |
| A flexible reference picture management scheme | 0 | 0.34 | 2014 |
