CodeFormer

CodeFormer is an AI photo and video restoration tool based on deep learning technology.

Products

CodeFormer, jointly developed by Nanyang Technological University (NTU) and Shangtang Technology, combines the cutting-edge technologies of Variable Quadrature Auto Encoder (VQGAN) and Transformer. It can significantly improve the quality and visual effect of images and videos through high-resolution reconstruction and detail restoration. The product is not only suitable for single and multi-person image processing, but also features colorization and breakage repair, and its Transformer model enhances robustness to deal with a variety of complex face image and video problems.

Key Features

1. Facial Restoration: Efficiently restore low-quality, blurred or damaged facial images, including removing noise, repairing damaged areas, and more.
2. facial enhancement: Significantly improves the clarity of images by enhancing the detail and contrast of images, making facial features more prominent.
3. Image Super Resolution: Converts a low-resolution facial image into a high-resolution image that retains more detailed information so that the image remains visible when magnified.
4. Emoji Fix: Processes facial images in motion video, fixes and enhances facial expressions to make character expressions in video more realistic and natural.
5. Colorization and damage repair: Colorize black-and-white images or monochrome background images and repair broken or destroyed images.
6. video enhancement: Handle blurring, jittering, color distortion in videos, improve video clarity and stability, and support super-resolution reconstruction of videos.

De-mosaic function

CodeFormer's de-mosaicing feature is one of its many powerful features that focuses on eliminating mosaic areas in images and videos to restore the clarity and detail of the original image.

Technical Principles

• Based on deep learning: CodeFormer utilizes advanced deep learning techniques, in particular an architecture that combines Variational Autoencoder (VQGAN) and Transformer. This combination allows the model to learn and predict the missing information in an image to effectively remove the mosaic.
• Code Sequence Prediction: By discretizing the codebook space through VQGAN, CodeFormer willImage Restorationtasks into code sequences for prediction tasks. This approach reduces the uncertainty in the mapping of the repair task and provides rich face details for the repair task.
• global modeling: Transformer's global modeling capabilities enable the model to capture global information in the image, further enhancing the de-mosaicing effect.

Functional Features

• Efficient mosaic removalCodeFormer: CodeFormer is able to perform precise removal of mosaic areas in images and videos, restoring sharpness and details close to the original image.
• Keeping it natural and real: While removing mosaics, CodeFormer maintains the natural and realistic look of the image, avoiding over-restoration or distortion.
• Supports multiple scenarios: The feature is suitable for a wide range of scenarios, including family album restoration, social media photo optimization, and professional image processing. Whether it's an old photo or a modern shot, CodeFormer provides excellent de-mosaicing results.

Usage Scenarios

1. Photography and Retouching: Photographers and retouchers can use CodeFormer to quickly fix and beautify the photos they take, improve the quality of photos and save time on manual retouching.
2. Video Production: During video production, CodeFormer can be used to repair and enhance facial images in videos, improving the overall quality and visual effect of the video.
3. Security & Surveillance: In the field of security and surveillance, CodeFormer can repair and enhance low-quality surveillance video, improve the accuracy of facial recognition, and help quickly identify and locate a target person.
4. Medical & Plastic: In the medical and plastic surgery fields, CodeFormer can be used for facial image restoration and simulation, helping doctors and patients make more accurate diagnoses and decisions by enhancing and beautifying images.
5. social media: Social media users can use CodeFormer to fix and beautify selfies and personal photos to enhance their personal image and increase the attractiveness of their photos.

Operating Instructions

1. environmental preparation: Ensure that your local computer has Git, Python, and the necessary libraries (such as TensorFlow or PyTorch) installed.
2. Download source code: Download CodeFormer's source code from code hosting platforms like GitHub.
3. Creating a Virtual Environment: Create a new Python virtual environment using tools such as conda or virtualenv to avoid dependency conflicts.
4. Installation of dependencies: Install the necessary Python dependencies according to the official documentation or the requirements.txt file.
5. configuration model: Download the pre-trained model weights file and configure the model path.
6. running program: Run CodeFormer according to the official documentation or sample code to repair and enhance the input facial image.

caveat

1. Graphics card requirements: Recommended to use GTX 1060 or above graphics cards, A-card acceleration is not supported.
2. Image and video formats: When dealing with video, make sure the video format is correct; when dealing with images, except for multiplayer image enhancement, the rest of the options need to crop the image to a resolution size of 512×512 first.
3. processing speed: Processing speed is affected by the performance of the graphics card, and high-performance graphics cards can significantly increase processing speed.