This repo is the official implementation of the following paper:
"PPI Edge Infused Spatial–Spectral Adaptive Residual Network for Multispectral Filter Array Image Demosaicing" (TGRS 2023) [Paper].
We present a pseudo-panchromatic image (PPI) edge-infused spatial–spectral adaptive residual network (PPIE-SSARN) for MSFA image demosaicing.
The proposed two-branch model deploys a residual subbranch to adaptively compensate for the spatial and spectral differences of reconstructed multispectral images and a PPI edge infusion subbranch to enrich the edge-related information. Moreover, we design an effective mosaic initial feature extraction module with a spatial- and spectral-adaptive weight-sharing strategy whose kernel weights can change adaptively with spatial locations and spectral bands to avoid artifacts and aliasing problems.
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ARAD-1K Dataset
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Chikusei Dataset
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Clone this repo:
git clone https://github.com/bowenzhao-zju/PPIE-SSARN cd PPIE-SSARN
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Build the DDMF module:
cd model/ddf python setup.py install mv build/lib*/* .
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Set the data path and the hyperparameters for training in
config.py
. -
Run
train.py
:python train.py
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Download the weights file
valid_best.pth
and place it incheckpoint
. -
Run
test.py
:python test.py
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The results are in
checkpoint/pred
.
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Set the data path and the weight file (
*.pth
) path for testing inconfig.py
. -
Run
test.py
:python test.py
Visual comparisons for demosaicing results on the ARAD-1K dataset. Our method is compared with five alternatives, that is, MCAN [Code], DPDNet [Code], 3D-ResNet, PPID [Code], and WB. The first column shows the GT. In addition, zoomed-in views of selected regions are provided. Please zoom in to see the details.
If you find the code and datasets helpful in your research work, please cite the following paper:
@ARTICLE{10188849,
author={Zhao, Bowen and Zheng, Jiesi and Dong, Yafei and Shen, Ning and Yang, Jiangxin and Cao, Yanlong and Cao, Yanpeng},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={PPI Edge Infused Spatial–Spectral Adaptive Residual Network for Multispectral Filter Array Image Demosaicing},
year={2023},
volume={61},
number={},
pages={1-14},
doi={10.1109/TGRS.2023.3297250}}