HDR Reconstruction from a Single Raw Image

Organized by BIT-CV - Current server time: Jan. 9, 2025, 11:16 p.m. UTC

First phase

valid
Feb. 20, 2024, midnight UTC

End

Competition Ends
April 30, 2024, 11:59 p.m. UTC

PBDL2024:HDR Reconstruction from a Single Raw Image Challenge

News!

  • The HDR Reconstruction from a Single Raw Image track starts now! We release testing data. Check out the "Get data" page and prepare the submission!

Important dates

  • 2024.02.20 Challenge site online

  • 2024.02.21 Release of train data (paired images) and validation data (inputs only)

  • 2024.03.01 Validation server online

  • 2024.04.23 Final test data release (inputs only)

  • 2024.04.30 Test submission deadline

  • 2024.05.05 Fact sheets and code/executable submission deadline

  • 2024.05.10 Preliminary test and rating results release to participants

Overview

The dynamic range of real-world scenes frequently exceeds the capture capabilities of standard consumer camera sensors, often resulting in loss of detail in both overly bright and dark areas. To address this, the computational imaging community has extensively explored High Dynamic Range (HDR) imaging, which records a broader spectrum of intensity levels and captures more scene information. Unlike conventional Low Dynamic Range (LDR) images, HDR preserves greater detail in both over- and under-exposed areas. This enhancement not only benefits various vision tasks, such as segmentation and object detection but also produces more visually pleasing images — a goal long pursued by computer vision researchers.

To further push the research of HDR reconstruction forward, we are launching a challenge centered on reconstructing HDR images from single raw images. This approach specifically focus on single raw image HDR reconstruction, which avoids potential misalignments that can occur in multi-image fusion. We will use the Raw-to-HDR dataset called SRHDR, mainly curated by Prof. Fu’s team in [a]. This dataset contains paires of LDR and HDR images. The LDR input is captured under challenging lighting conditions, representing the over- and under-exposed regions of a high dynamic range scene. The corresponding ground truth HDR images in the dataset are produced through bracketed exposures of each scene, subsequently merged using basic HDR fusion algorithms (Debevec etal., 2008). We will host the competition using open source online platform, e.g. CodaLab. All submissions are evaluated by our script running on the server and we will double check the results of top-rank methods manually before releasing the final test-set rating.

Submission

The train and validation data is already made available to the registered participants.

General Rules

Please check the terms and conditions for further rules and details.

Reference

Contact Us

Please test the submission process through the validation (val) phase to ensure smooth submission. If there are any bugs, please contact us promptly during the validation phase. Once the testing phase begins, to ensure fairness, we will no longer address issues related to result submissions. You can contact us by sending an email to organizers pbdl.ws@gmail.com with title 'HDR Reconstruction from a Single Raw Image Inquiry'.

Evaluation Criteria

We employ the standard Peak Signal To Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) in grayscale, as is commonly used in the literature. The final evaluation metric can be calculated using the following formula:

$$Score=\log_k(SSIM*k^{PSNR})+\log_k(MS-SSIM*k^{PSNR-µ})=PSNR+\log_k(SSIM)+PSNR-µ+\log_k(MS-SSIM)$$

In our implementation, $k=1.2$.

For the final ranking, we will use the average Score as the primary measure. The complexity of the algorithm will only serve as a reference and will not be included in the final metric. Please refer to the evaluation function in the 'evaluate.py' of the scoring program.

Submission

During the development phase, the participants can submit their results on the validation set to the CodaLab server. The validation set should only be used for evaluation and analysis purposes but NOT for training. At the testing phase, the participants will submit the whole restoration results of the test set. This should match the last submission to the CodaLab.

Terms and Conditions

General Rules

The HDR Reconstruction from a Single Raw Image Challenge is one track of PBDL-Challenge, Physics Based Vision meets Deep Learning Workshop 2024, in conjunction with CVPR 2024. Participants are not restricted to train their algorithms only on the provided dataset. Other PUBLIC dataset can be used as well. Participants are expected to develop more robust and generalized methods for low light image enhancement in real-world scenarios.

When participating in the competition, please be reminded that:

  • Results in the correct format must be uploaded to the evaluation server. The evaluation page lists detailed information regarding how results will be evaluated.
  • Each entry must be associated to a team and provide its affiliation.
  • Using multiple accounts to increase the number of submissions and private sharing outside teams are strictly prohibited.
  • The organizer reserves the absolute right to disqualify entries which is incomplete or illegible, late entries or entries that violate the rules.
  • The organizer reserves the right to adjust the competition schedule and rules based on situations.
  • The best entry of each team will be public in the leaderboard at all time.
  • To compete for awards, the participants must fill out a factsheet briefly describing their methods. There is no other publication requirement.

Terms of Use: Dataset

Before downloading and using the dataset, please agree to the following terms of use. You, your employer and your affiliations are referred to as "User". The organizers and their affiliations, are referred to as "Producer".

  • All the data is used for non-commercial/non-profit research purposes only.
  • All the images in the dataset can be used for academic purposes.
  • The User takes full responsibility for any consequence caused by his/her use of the dataset in any form and shall defend and indemnify the Producer against all claims arising from such uses.
  • The User should NOT distribute, copy, reproduce, disclose, assign, sublicense, embed, host, transfer, sell, trade, or resell any portion of the dataset to any third party for any purpose.
  • The User can provide his/her research associates and colleagues with access to dataset (the download link or the dataset itself) provided that he/she agrees to be bound by these terms of use and guarantees that his/her research associates and colleagues agree to be bound by these terms of use.
  • The User should NOT remove or alter any copyright, trademark, or other proprietary notices appearing on or in copies of the dataset.
  • This agreement is effective for any potential User of the dataset upon the date that the User first accesses the dataset in any form.
  • The Producer reserves the right to terminate the User's access to the dataset at any time.
  • For using the dataset, please consider citing the paper (if any):

@inproceedings{zou2023rawhdr,
    title={Rawhdr: High dynamic range image reconstruction from a single raw image},
    author={Zou, Yunhao and Yan, Chenggang and Fu, Ying},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
    pages={12334--12344},
    year={2023}
}

Reproducibility

Industry and research labs are allowed to submit entries and to compete in both the validation phase and the final test phase. However, in order to get officially ranked on the final test leaderboard and to be eligible for awards the reproducibility of the results is a must and, therefore, the participants need to make available and submit their codes or executables. All the top entries will be checked for reproducibility and marked accordingly.

 

valid

Start: Feb. 20, 2024, midnight

Description: The online evaluation results must be submitted through this CodaLab competition site of the Challenge.

test

Start: April 23, 2024, midnight

Description: The online evaluation results must be submitted through this CodaLab competition site of the Challenge.

Competition Ends

April 30, 2024, 11:59 p.m.

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