SYNDOF (Synthetic Defocus Blur Image Dataset)
Paper | Supp | DMENet Repo
This repository contains the official matlab implementation of SYNDOF generation used in the following paper:
Deep Defocus Map Estimation using Domain Adaptation
Junyong Lee1, Sungkil Lee2, Sunghyun Cho3, and Seungyong Lee1
1POSTECH, 2Sungkyunkwan University, 3DGIST
IEEE Computer Vision and Pattern Recognition (CVPR) 2019
Picture: Outputs generated from our code– from left to right, synthetic input, defocus map output and defocused image.
Tested environment
- Download and unzip the synthetic datasets (OneDrive | Dropbox) under
./data
:├── data │ ├── synthetic_datasets │ │ ├── ...
-
On matlab console, type
# max_coc, input_offset, output_offset, is_random_gen, is_gpu, gpu_num generate_blur_by_depth(29, 'data', 'out', false, true, 1)
-
check the results under
./out
, which is structured as,├── ... ├── out │ ├── blur_map/ # directory for output defocus map │ ├── blur_map_binary/ # directory for binarized defocus map │ ├── blur_map_norm/ # directory for normalized defocus map │ ├── depth_decomposed/ # directory for decomposed depth │ ├── image/ # directory for input image (with its modified name)
- We rounded real values of defocus map into the nearest 10-th. When you read a defocus map, for example in python, read the file as follows:
image = (np.float32(cv2.imread(file_name, cv2.IMREAD_UNCHANGED))/10.)[:, :, 1] image = image / 7. # 7 = (maxCoC - 1) / 4, where maxCoC is 29 in this case.
Open an issue for any inquiries. You may also have contact with junyonglee@postech.ac.kr
This software is being made available under the terms in the LICENSE file.
Any exemptions to these terms require a license from the Pohang University of Science and Technology.
If you find this code useful, please consider citing:
@InProceedings{Lee2019DMENet,
author = {Junyong Lee and Sungkil Lee and Sunghyun Cho and Seungyong Lee},
title = {Deep Defocus Map Estimation Using Domain Adaptation},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019}
}