Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao

Abstract: The availability of large-scale datasets has helped unleash the true potential of deep convolutional neural networks (CNNs). However, for the single-image denoising problem, capturing a real dataset is an unacceptably expensive and cumbersome procedure. Consequently, image denoising algorithms are mostly developed and evaluated on synthetic data that is usually generated with a widespread assumption of additive white Gaussian noise (AWGN). While the CNNs achieve impressive results on these synthetic datasets, they do not perform well when applied on real camera images, as reported in recent benchmark datasets. This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline. In this paper, we present a framework that models camera imaging pipeline in forward and reverse directions. It allows us to produce any number of realistic image pairs for denoising both in RAW and sRGB spaces. By training a new image denoising network on realistic synthetic data, we achieve the state-of-the-art performance on real camera benchmark datasets. The parameters in our model are ~5 times lesser than the previous best method for RAW denoising. Furthermore, we demonstrate that the proposed framework generalizes beyond image denoising problem e.g., for color matching in stereoscopic cinema.

The proposed CycleISP framework allows converting sRGB images to RAW data, and then back to sRGB images. It has (a) RGB2RAW network branch, and (b) RAW2RGB network branch.

Overall Framework of CycleISP

Recursive Residual Group (RRG)

The model is built in PyTorch 1.1.0 and tested on Ubuntu 16.04 environment (Python3.7, CUDA9.0, cuDNN7.5).

For installing, follow these intructions

sudo apt-get install cmake build-essential libjpeg-dev libpng-dev
conda create -n pytorch1 python=3.7
conda activate pytorch1
conda install pytorch=1.1 torchvision=0.3 cudatoolkit=9.0 -c pytorch
pip install matplotlib scikit-image yacs lycon natsort h5py tqdm

• Download all the models and place them in ./pretrained_models/isp/
• Download some sample images from the MIR-Flickr dataset and place them in ./datasets/some_sample_images/

The RGB2RAW network branch takes as input a clean sRGB image and converts it to a clean RAW image. The noise injection module adds shot and read noise of different levels to the (RAW) output of RGB2RAW network branch. Thereby, we can generate clean and its corresponding noisy image pairs {RAW_clean, RAW_noisy} from any sRGB image.

• Run Demo

python generate_raw_data.py 

Given a synthetic RAW noisy image as input, the RAW2RGB network branch maps it to a noisy sRGB image; hence we are able to generate an image pair {sRGB_clean, sRGB_noisy} for the sRGB denoising problem.

• Run Demo

python generate_rgb_data.py 

You can download, at once, the complete repository of CycleISP (including pre-trained models, datasets, results, etc) from this Google Drive link, or evaluate individual tasks with the following instructions:

• Download the model and place it in ./pretrained_models/denoising/
• Download RAW images of DND and place them in ./datasets/dnd/dnd_raw/
• Run

python test_dnd_raw.py --save_images

• Download the model and place it in ./pretrained_models/denoising/
• Download RAW images of SIDD and place them in ./datasets/sidd/sidd_raw/
• Run

python test_sidd_raw.py --save_images

• Download the model and place it in ./pretrained_models/denoising/
• Download sRGB images of DND and place them in ./datasets/dnd/dnd_rgb/noisy/
• Run

python test_dnd_rgb.py --save_images

• Download the model and place it in ./pretrained_models/denoising/
• Download sRGB images of SIDD and place them in ./datasets/sidd/sidd_rgb/
• Run

python test_sidd_rgb.py --save_images

Experiments are performed for denoising images in RAW and sRGB spaces. Results produced by CycleISP can be downloaded from Google Drive link.

If you use CycleISP, please consider citing:

@inproceedings{Zamir2020CycleISP,
    title={CycleISP: Real Image Restoration via Improved Data Synthesis},
    author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat
            and Fahad Shahbaz Khan and Ming-Hsuan Yang and Ling Shao},
    booktitle={CVPR},
    year={2020}
}

Should you have any question, please contact waqas.zamir@inceptioniai.org

-- Learning Enriched Features for Fast Image Restoration and Enhancement, TPAMI 2022. Paper | Code

• Restormer: Efficient Transformer for High-Resolution Image Restoration, CVPR 2022. Paper | Code
• Multi-Stage Progressive Image Restoration, CVPR 2021. Paper | Code
• Learning Enriched Features for Real Image Restoration and Enhancement, ECCV 2020. Paper | Code