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- Overview
- Requirements
- Usage
- Citation
Attention-guided Network for Ghost-free High Dynamic Range Imaging (AHDR)
Qingsen Yan*, Dong Gong*, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Ian Reid, Yanning Zhang. In IEEE Conference on Compute rVision and Pattern Recognition (CVPR), 2019:1751-1760. (* Equall contribution) [Paper][Project]
Data preparation
1.Download data from [dataset] 2.Move the dataset into ./GenerH5Data/TrainingData. 3.Run ./GenerH5Data/PrepareData.m
Testing
1.Install this repository and the required packages. A pretrained model is in ./trained-model. 2.Prepare dataset. 3.Download dataset. 4.Move the dataset into ./dataset. 5.Processed dataset can be obtained by running the corresponding script in ./GenerH5Data/PrepareData.m. 1.Run python script_testing.py files.
Training
1.Prepare dataset. 2.Download dataset. 3.Move the dataset into ./dataset. 4.Processed dataset can be obtained by running the corresponding script in ./GenerH5Data/PrepareData.m. 1.Run python script_training.py files.
If you use this code for your research, please cite our paper.
23 avr. 2019 · To avoid the ghosting from the source, we propose a novel attention-guided end-to-end deep neural network (AHDRNet) to produce high-quality ghost-free HDR images. Unlike previous methods directly stacking the LDR images or features for merging, we use attention modules to guide the merging according to the reference image.
- Qingsen Yan, Dong Gong, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Ian Reid, Yanning Zhang
- arXiv:1904.10293 [cs.CV]
- 2019
- Accepted to appear at CVPR 2019
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Inspired by the fact, we propose an attention-guided denoising convolutional neural network (ADNet), mainly including a sparse block (SB), a feature enhancement block (FEB), an attention block (AB) and a reconstruction block (RB) for image denoising.
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