Unfolding Taylor's Approximations for Image Restoration
Authors: man zhou, Xueyang Fu, Zeyu Xiao, Gang Yang, Aiping Liu, Zhiwei Xiong
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments To demonstrate the effectiveness and scalability of our proposed framework, we conduct experiments over two image restoration tasks, i.e., image deraining and image deblurring. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China {manman}@mail.ustc.edu.cn xyfu@ustc.edu.cn |
| Pseudocode | No | Figure 1: The overview structure of Deep Taylor s Approximations Framework. |
| Open Source Code | No | Code will be publicly available upon acceptance. |
| Open Datasets | Yes | Regarding experiment rainy datasets, the first three methods including DDN, DCM and PRe Net are trained and evaluated over three widelyused standard benchmark datasets, including Rain100H, Rain100L, Rain800. ...For image deblurring task, typical methods like DCM [14], MSCNN [30] and RDN [66, 65] are adopted in our experiments. As in [62, 45, 26], we use the Go Pro [30] dataset that contains 2,103 image pairs for training and 1,111 pairs for evaluation. Furthermore, to demonstrate generalizability, we take the model trained on the Go Pro dataset and directly apply it on the test images of the HIDE [41] and Real Blur [39] datasets. |
| Dataset Splits | No | For image deraining task... Rain100H, a heavy rainy dataset are composed with 1,800 rainy images for training and 100 rainy samples for testing. ...Rain100L dataset contains 200 training samples and 100 testing images... the Rain800 is proposed in [58], which includes 700 training and 100 testing images. ...For image deblurring task... we use the Go Pro [30] dataset that contains 2,103 image pairs for training and 1,111 pairs for evaluation. |
| Hardware Specification | Yes | One NVIDIA GTX 2080Ti GPU is used for training. |
| Software Dependencies | No | The ADAM algorithm is adopted to train the models with an initial learning rate 1 10 3, and ends after 100 epochs. |
| Experiment Setup | Yes | The patch size is 100 100, and the batch size is 4. The ADAM algorithm is adopted to train the models with an initial learning rate 1 10 3, and ends after 100 epochs. When reaching 30, 50 and 80 epochs, the learning rate is decayed by multiplying 0.2 and λ is set as 1.0 in loss function. |