Real-time Image Demoiréing on Mobile Devices

Pytorch implementation of our paper accepted by ICLR 2023 -- "Real-time Image Demoiréing on Mobile Devices".

Requirements

• python 3.7
• pytorch 1.9.0
• torchvision 0.11.3
• opencv-python-headless 4.6
• colour 0.1.5
• scikit-image 0.19.3

Taining

First, split and divide the training dataset by the following command:

sh run/split_dataset.sh

Note that the data path of demoiréing benchmarks should be modified in /data_script/aim/slit_patches_train.py

Second, run the command scripts in run/ to accelerate models on different benchmarks. For example, to reproduce the results of DDA for accelerating MBCNN on FHDMI, run:

sh run/mbcnn_fhdmi.sh

Evaluating

The checkpoint file of the accelerated models are provided in the following anonymous link. To evaluate them, download the model file and place it into /ckpt and then run the command script in run/. For example, to evaluate the accelerated model of DDA for accelerating MBCNN on FHDMI, run:

sh run/test_mbcnn_fhdmi.sh

Model	Dataset	PSNR	FLOPs reduction	Link
DMCNN	LCDMoiré	34.19	0%	Link
DMCNN-DDA	LCDMoiré	34.58	55.1%	Link
DMCNN	FHDMI	21.69	0%	Link
DMCNN-DDA	FHDMI	21.86	52.3%	Link
MBCNN	LCDMoiré	43.95	0%	Link
MBCNN-DDA	LCDMoiré	41.68	46.9%	Link
MBCNN	FHDMI	23.27	0%	Link
MBCNN-DDA	FHDMI	23.62	45.2%	Link

Any problem, feel free to contact yuxinzhang@stu.xmu.edu.cn