Three AI-Powered Image Restoration Tools for Face Enhancement, Watermark Removal, and Wrinkle/Skin Tone Correction

You know the struggle: digging out old photos only to find faces blurred beyond recognition; finally capturing a scenic shot, only to spot an annoying trash bin in the corner; trying to upscale a low-res meme, but every tool is either paid or makes it look worse. Say no more—here are three open-source AI image restoration tools, ranked by popularity, that handle everything from old photo repair to watermark removal. You haven’t seen a roundup like this before!

---

1. GFPGAN
In-Depth Description
This tool is the ultimate solution for AI face restoration! It specializes in repairing blurry, distorted faces—whether in decades-old photos or heavily compressed selfies. It reconstructs facial features with stunning clarity, even restoring fine details like strands of hair. The developers clearly prioritize preserving authenticity: it subtly enhances wrinkles and skin tones without making faces look artificially retouched or unlike themselves.

Key AI Image Restoration Features
Repairs blurry faces, enhances old photos, upscales low-resolution portraits (up to 4x), and corrects minor facial distortions. Outputs retain original features without excessive retouching. Supports batch processing and can be integrated with other tools for real-time video face restoration.

Ideal Use Cases
Restoring cherished old family photos, upscaling compressed memes or idol images, improving low-quality selfies from old phones, and even repairing blurry faces in videos (with additional tools).

How to Use
Easy-to-follow steps for beginners using Python:

bash

# Clone the repository
git clone https://github.com/TencentARC/GFPGAN.git
cd GFPGAN

# Install dependencies (recommended: use a virtual environment)
pip install -r requirements.txt
pip install basicsr facexlib
pip install gfpgan

# Run restoration (example: process images in 'inputs', save to 'results')
python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.4 -s 2
# -i: input folder, -o: output folder, -s: scale factor

Pros & Cons
Pros: Natural-looking face repair, excellent detail recovery, user-friendly.
Cons: Focused only on faces (not landscapes/objects); requires decent hardware for batch processing.

Project Link
https://github.com/TencentARC/GFPGAN

---

2. Real-ESRGAN
In-Depth Description
If GFPGAN is the “face expert,” this is the “all-rounder”! It not only restores blurry images but also performs super-resolution—upscaling a 300×300 image to 1200×1200 while maintaining clarity. It reconstructs fine details like small text and textures, and even removes compression artifacts and noise. Perfect for detail enthusiasts.

Key AI Image Restoration Features
Image super-resolution (2x/4x/8x upscaling) with deblurring, compression artifact removal, and noise reduction. Supports old photos and low-res video frames. Includes an “anime mode” for preserving clean lines in cartoon images. Batch processing and comparison image generation are also supported.

Ideal Use Cases
Enlarging and enhancing old low-resolution photos, upscaling memes for better clarity, improving video screenshots, and restoring scanned documents for readable text.

How to Use
Simple Python implementation for beginners:

# Clone the repo

git clone https://github.com/xinntao/Real-ESRGAN.git
cd Real-ESRGAN

# Install dependencies

pip install -r requirements.txt
pip install realesrgan

# Download pre-trained model (or let it auto-download)

wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P weights

# Run restoration (example: single image, 4x scale)

# -n: model name, -i: input, -o: output, -s: scale

Pros & Cons
Pros: Versatile (super-resolution + restoration), works on both real-world and anime content.
Cons: Slow for large images, high GPU demand for 8x upscaling; may occasionally struggle with complex textures.

Project Link
https://github.com/xinntao/Real-ESRGAN

---

3. LaMa
In-Depth Description
This is the ultimate “image eraser”! It seamlessly removes watermarks, passersby, scratches, or any unwanted elements from photos. Just mask the area, and LaMa intelligently inpaints the background to match the original scene. The developers have truly delivered an OCD-friendly tool—even complex backgrounds like skies or grass are reconstructed naturally.

Key AI Image Restoration Features
Removes unwanted objects, watermarks, and scratches with context-aware inpainting. Handles large images without quality loss, offers fast processing, and works well with textured backgrounds. Supports batch processing for efficient watermark removal.

Ideal Use Cases
Removing tourists from travel photos, deleting watermarks or logos, repairing scratches and creases in old photos, and cleaning up text in memes for re-editing.

How to Use
Docker is recommended for easier setup:

# Clone the repository

git clone https://github.com/advimman/lama.git
cd lama

# Build the Docker image

docker build -t lama .

# Run restoration (input: original images; mask: white=remove, black=keep)

docker run -v $(pwd)/input:/input -v $(pwd)/mask:/mask -v $(pwd)/output:/output lama python3 bin/predict.py model.path=big-lama indir=/input outdir=/output maskdir=/mask

Pros & Cons
Pros: Natural inpainting, strong background reconstruction, great for large-area removal.
Cons: Requires manual masking; less effective on small details compared to GFPGAN.

Project Link
https://github.com/advimman/lama