Night Photography Rendering Challenge

This challenge tackles the complexities of nighttime photography by leveraging paired datasets of raw Huawei smartphone images and processed Sony camera images, providing a clear ground truth for evaluation. The goal is to develop algorithms that process raw Huawei images to convincingly resemble the high-quality Sony outputs, addressing a long-standing challenge in computer vision.

Night photography is vital for applications like surveillance and security and also has artistic significance in creating stunning images. By combining objective ground-truth comparisons with human perception through mean opinion scores, the challenge ensures that results are both technically accurate and visually convincing.

This unique framework bridges mobile device constraints, low-light conditions, and human-centric evaluation, advancing the state of the art in night image processing.

Submissions will be evaluated by mean opinion scores and objective metrics.

This repo contains the source code of Night Photography Rendering Challenge 2025.

Participants' solutions

All solutions submitted to the final stage of the challenge are available for download via link (Google Drive).

Initial data processing

File crop_resize.py contains function "def crop_resize" which allows to bring source images from smartphone to size of ground truth images from photo camera. CAUTION! Crop and resize should be performed after debayering and distortion correction of input images.

Baseline

Raw images with metadata should be placed in the /data directory.

Installation and requirements

To work with code it is recommended to use Python 3.9+. The required packages are listed in requirements.txt and can be installed by calling:

pip install -r requirements.txt

raw_prc_pipeline

Module raw_prc_pipeline contains the source code of various methods and functions that can be used for processing raw images, including:

• Parsing metadata (see raw_prc_pipeline/exif_*.py files)
• Demosaicing, white_balancing (Gray World, White Patch, Shades of Gray, Improved White Patch), tone mapping and other methods (see raw_prc_pipeline/pipeline_utils.py)
• Implemtations of demo classes of raw image processing pipeline and image processing pipeline executor (see raw_prc_pipeline/pipeline.py)

Caution If color_matrix_* is not provided average color matrix of Huawei Mate 40 Pro is used instead.

demo

Directory demo contains demonstration script for processing PNG raw images with JSON metadata using implemented classes and finctions from raw_prc_pipeline.

To process PNG images with corresponding metadata from data directory call the following command:

python -m demo.process_pngs -p data -ie gw -tm Flash

To see other arguments of the script call python -m demo.process_pngs -h from the root directory.

Also you can use more reproducible way via Docker:

sudo docker build -t nightimaging .
sudo docker run --rm -u $(id -u):$(id -g) -v $(pwd)/data:/data nightimaging ./run.sh