How to Use CycleISP for Real Image Restoration via Improved Data Synthesis

In the evolving realm of image processing, the CycleISP framework has emerged as a groundbreaking tool for image denoising. This guide will walk you through the process of using CycleISP to restore your images, troubleshoot common issues, and bolster your understanding of its functionalities.

Understanding CycleISP: A Brief Analogy

Imagine you have a friend who loves painting but often makes mistakes, creating artwork that looks smudged and unrefined. To help them, you develop a special program where they can take their revisions (sRGB images), digitally convert them into a clean canvas (RAW images), make the necessary corrections, and then return to the original colorful world, with a refined piece (again sRGB). Similarly, CycleISP converts sRGB images into RAW images and back, making it easier to create paired images to train denoising networks effectively.

Setting Up Your Environment

CycleISP is built using PyTorch version 1.1.0 and is optimized for Ubuntu 16.04. Follow the installation steps below to get started:

• Install necessary dependencies:

sudo apt-get install cmake build-essential libjpeg-dev libpng-dev

• Create a conda environment:

conda create -n pytorch1 python=3.7

• Activate the environment:

conda activate pytorch1

• Install PyTorch and other required libraries:

conda install pytorch=1.1 torchvision=0.3 cudatoolkit=9.0 -c pytorch
pip install matplotlib scikit-image yacs lycon natsort h5py tqdm

Data Synthesis with CycleISP

To generate realistic datasets for image denoising, follow these steps:

• Download the [CycleISP models](https://drive.google.com/drive/folders/1cHRfWPjFpTPrvBm7B4iTMEVZ4ABfjmxW?usp=sharing) and place them in `.pretrained_models/isp`.
• Download sample images from the MIR-Flickr dataset and place them in `.datasets/some_sample_images`.

Generating Data for RAW Denoising

To generate paired RAW images:

python generate_raw_data.py

Generating Data for sRGB Denoising

For generating noise-free sRGB images:

python generate_rgb_data.py

Evaluation of the Denoising Process

Your restoration results can be evaluated on various datasets by downloading the appropriate models and executing the provided scripts. The following steps outline how to evaluate both RAW and sRGB images:

• Denoising RAW images from DND – [Download model](https://drive.google.com/drive/folders/15Bay1UJURlbP7kpS10_fJ96MxQ6B03Xv?usp=sharing) and run:

python test_dnd_raw.py --save_images

• Denoising RAW images from SIDD – [Download model](https://drive.google.com/drive/folders/1m2A4goZENg_kKV1-FJoeg1rBqLj6WRIm?usp=sharing) and run:

python test_sidd_raw.py --save_images

• Denoising sRGB images from DND – [Download model](https://drive.google.com/drive/folders/1740sYH7bG-c-jL5wc3e1_NOpxwGTXS9c?usp=sharing) and run:

python test_dnd_rgb.py --save_images

• Denoising sRGB images from SIDD – [Download model](https://drive.google.com/drive/folders/1sraG9JKmp0ieLjntRL7Jj2FXBrPr-YVp?usp=sharing) and run:

python test_sidd_rgb.py --save_images

Troubleshooting

If you encounter issues, consider the following troubleshooting ideas:

• Ensure that all dependencies are installed correctly and paths are properly set up for datasets and models.
• If you face runtime errors, verify compatibility with Python versions and libraries.
• Check your dataset for completeness—missing images can lead to processing errors.

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Conclusion

By following this guide, you should be well-equipped to leverage the CycleISP framework for your image restoration endeavors. The results can exceed expectations, especially when paired with a clear understanding of its functionality.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.