Real-Time Image Demoir$\acute{e}$ing on Mobile Devices
Authors: Yuxin Zhang, Mingbao Lin, Xunchao Li, Han Liu, Guozhi Wang, Fei Chao, Ren Shuai, Yafei Wen, Xiaoxin Chen, Rongrong Ji
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmarks demonstrate the efficacy of our proposed DDA. In addition, the acceleration evaluated on the VIVO X80 Pro smartphone equipped with a chip of Snapdragon 8 Gen 1 shows that our method can drastically reduce the inference time, leading to a real-time image demoir eing on mobile devices. |
| Researcher Affiliation | Collaboration | Yuxin Zhang1 , Mingbao Lin1, Xunchao Li1, Han Liu2, Guozhi Wang2, Fei Chao1, Shuai Ren2, Yafei Wen2, Xiaoxin Chen2, Rongrong Ji1,3,4 1MAC Lab, School of Informatics, Xiamen University, 2VIVO AI Lab, 3Institute of Artificial Intelligence, Xiamen University, 4Pengcheng Lab |
| Pseudocode | No | The paper describes the methodology in text and with diagrams, but does not include pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source codes and models are released at https://github.com/zyxxmu/DDA. |
| Open Datasets | Yes | There are three main public datasets for image demoir eing. (1) LCDMoir e dataset (Yuan et al., 2019) from the AIM19 image demoir eing challenge consists of 10,200 synthetically generated image pairs including 10,000 training images, 100 validation images and 100 testing images at 1024 1024 resolution. (2) FHDMi dataset (He et al., 2020) contains 9,981 image pairs for training and 2,019 for testing with 1920 1080 resolution. |
| Dataset Splits | Yes | (1) LCDMoir e dataset (Yuan et al., 2019) from the AIM19 image demoir eing challenge consists of 10,200 synthetically generated image pairs including 10,000 training images, 100 validation images and 100 testing images at 1024 1024 resolution. (2) FHDMi dataset (He et al., 2020) contains 9,981 image pairs for training and 2,019 for testing with 1920 1080 resolution. |
| Hardware Specification | Yes | In addition, the acceleration evaluated on the VIVO X80 Pro smartphone equipped with a chip of Snapdragon 8 Gen 1 shows that our method can drastically reduce the inference time, leading to a real-time image demoir eing on mobile devices. ... All experiments are run on NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | Our implementation of DDA is based on the Py Torch framework (Paszke et al., 2019), with the group number M = 3, width list W = {0.25, 0.5, 0.75} on FHDMi and W = {0.4, 0.5, 0.6} on LCDMoir e. |
| Experiment Setup | Yes | The initial learning rate and batch size are set to 1e-4 and 4 in all experiments. During training, we iteratively extract a batch of image pairs within a specific class of moir e complexity, which are used to train the subnet of corresponding width extracted from the supernet. For DMCNN, we give 200 epochs for training with the learning rate divided by 10 at the 100-th epoch and 150-th epoch. For MBCNN, we follow (Zheng et al., 2020) to reduce the learning rate by half if the decrease in the validation loss is lower than 0.001 d B for four consecutive epochs and stop training once the learning rate becomes lower than 1e-6. |