Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring | 0 | 0.34 | 2022 |
Attentive Fine-Grained Structured Sparsity for Image Restoration | 0 | 0.34 | 2022 |
Recurrence-in-Recurrence Networks for Video Deblurring. | 0 | 0.34 | 2021 |
NTIRE 2021 Challenge on Video Super-Resolution | 3 | 0.37 | 2021 |
NTIRE 2021 Challenge on Image Deblurring | 4 | 0.40 | 2021 |
NTIRE 2020 Challenge on Image and Video Deblurring | 0 | 0.34 | 2020 |
Recurrent Neural Networks With Intra-Frame Iterations For Video Deblurring | 6 | 0.41 | 2019 |
NTIRE 2019 Challenge on Video Deblurring: Methods and Results | 2 | 0.37 | 2019 |
NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results | 1 | 0.36 | 2019 |
NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study | 3 | 0.37 | 2019 |
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. | 77 | 1.71 | 2018 |
Dynamic Video Deblurring Using a Locally Adaptive Blur Model. | 5 | 0.39 | 2018 |
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring | 58 | 1.54 | 2017 |
Enhanced Deep Residual Networks for Single Image Super-Resolution. | 244 | 4.46 | 2017 |
Dynamic Scene Deblurring using a Locally Adaptive Linear Blur Model. | 3 | 0.39 | 2016 |
Random forest with data ensemble for saliency detection. | 0 | 0.34 | 2015 |