Universal Watermark Removal

Remove watermarks from ANY image source using intelligent detection and proven methods. Smart routing: automatically detects Google SynthID and uses the proven aggressive method, falls back to ML inpainting for unknown watermark types.

Quick Start

Single Image - Smart Auto-Detection (Recommended)

Smart detection: Google SynthID → aggressive method (proven), Unknown → inpaint (ML)

python .claude/skills/watermark-removal/scripts/remove-watermark.py
input.png
output.png

Single Image - Preserve Dimensions (Force ML)

Force ML inpainting even for Google SynthID (preserves exact dimensions)

python .claude/skills/watermark-removal/scripts/remove-watermark.py
input.png
output.png
--method inpaint

Batch Processing (Get User Approval First!)

⚠️ IMPORTANT: Always ask user before batch processing, especially with crop/aggressive methods that alter dimensions.

Recommended: Preserves dimensions

python .claude/skills/watermark-removal/scripts/batch-process.py
/path/to/input-dir
/path/to/output-dir
--method inpaint

Alternative: Fast but crops 120px (requires user approval)

python .claude/skills/watermark-removal/scripts/batch-process.py
/path/to/input-dir
/path/to/output-dir
--method aggressive

Smart Detection System ⭐ NEW

The skill automatically detects Google SynthID watermarks and routes to the optimal removal method:

Google SynthID Detection

Characteristics analyzed:

• RGBA mode (PNG format with alpha channel)

• Large dimensions (>1500px width and height)

• Typical Google AI aspect ratios (1.83, 1.0, 1.5, 1.78 with 10% tolerance)

• Automatic corner detection for watermark location

Smart routing logic:

If Google SynthID detected → Uses aggressive method (proven to work perfectly)

• Crops 120px from detected corner

• Removes alpha channel watermarking

• Paints over any remnants

• Works 100% reliably on Google AI images

If unknown/other watermark → Uses inpaint method (ML-based)

• Preserves exact dimensions

• Uses OpenCV Navier-Stokes algorithm

• Works on any watermark type

Override Default Behavior

Force ML inpainting even for Google SynthID (preserves dimensions)

python scripts/remove-watermark.py input.png output.png --method inpaint

Force aggressive method for non-Google watermarks

python scripts/remove-watermark.py input.png output.png --method aggressive

Disable auto-detection (assume bottom-right corner)

python scripts/remove-watermark.py input.png output.png --no-detect

Methods

Inpaint Method (Best Quality) ⭐ NEW

What it does: ML-based inpainting with automatic watermark detection

Features:

• Automatically detects watermark location (any corner)

• Uses OpenCV's Navier-Stokes inpainting algorithm

• Intelligently fills watermark area with surrounding patterns

• Works on ANY watermark type (not just Google SynthID)

Pros:

• Highest quality results

• Preserves exact dimensions

• Works on watermarks in any corner

• Handles complex backgrounds intelligently

• Universal - works on all watermark types

Cons:

• Requires OpenCV installation (pip install opencv-python )

• Slightly slower than crop method

• May need parameter tuning for very large watermarks

Use when:

• You need the best possible quality

• Watermark is on complex/detailed background

• Preserving exact dimensions is critical

• Working with non-Google watermarks

Aggressive Method (Fast & Reliable)

What it does: Auto-detects corner, crops 120px, removes alpha channel, paints remnants

Pros:

• Fast and reliable

• Automatic detection of watermark corner

• Handles RGBA images properly

• Good for batch processing

Cons:

• Reduces image size by 120px

• May crop content near edges

Use when:

• Processing many images quickly (default for batch)

• Size reduction is acceptable

• Google SynthID watermarks

Crop Method

What it does: Auto-detects and crops watermark corner

Pros:

• Fastest method

• Automatic detection

• Minimal processing

Cons:

• May leave watermark remnants

• Doesn't handle alpha channel watermarking

Use when:

• Speed is top priority

• Quick preview needed

Paint Method

What it does: Paints over watermark without cropping (no auto-detection)

Pros:

• Preserves dimensions

• Simple approach

Cons:

• Assumes bottom-right corner only

• May leave visible artifacts

• Less reliable than inpaint

Use when:

• Simple watermarks on solid backgrounds

• Legacy compatibility

Important Guidelines

Dimension Preservation Priority

BALANCE DOMAIN KNOWLEDGE WITH DIMENSION PRESERVATION

Smart default behavior:

• Google SynthID detected → aggressive method (proven perfect, crops 120px)

• Unknown watermark → inpaint method (preserves dimensions)

User override available:

• Force dimension preservation with --method inpaint flag

• Force cropping with --method aggressive flag

User approval required for batch cropping:

• If processing multiple Google SynthID images with aggressive method

• Explain that method will reduce image size by 120px

• Get explicit confirmation

• Show before/after dimensions

Batch Processing Protocol

NEVER start batch processing without user confirmation:

Show what will happen:

• Number of images to process

• Method to be used

• Whether dimensions will be preserved or altered

Get explicit approval:

• "I will process X images using [method]. This [will/will not] alter dimensions. Proceed?"

Prefer non-destructive:

• Default to inpaint for batch processing

• Only use aggressive if user specifically requests speed over quality

Workflow

1. Identify Watermarked Images

Common watermark types:

• Google SynthID: Small star/sparkle icon in corner

• Stock photos: Logo or text overlay

• AI services: Corner badges (Midjourney, DALL-E)

• Camera watermarks: Date/time stamps

2. Choose Method

Smart Default Workflow (NEW):

• Run script without --method flag - Smart detection automatically routes to best method

• Google SynthID detected → Uses aggressive method (proven perfect)

• Unknown watermark → Uses inpaint method (ML-based, preserves dimensions)

• Override with --method flag if needed

Method Selection Guide:

• Smart auto (recommended): No flag (detects Google SynthID → aggressive, else → inpaint)

• Force dimension preservation: --method inpaint (ML-based, works on any watermark)

• Force Google method: --method aggressive (crops 120px, perfect for SynthID)

• Maximum speed: --method crop (fastest, crops but may leave remnants)

• Legacy: --method paint (basic, preserves dimensions but less reliable)

3. Process Images

Smart auto-detection (recommended):

python scripts/remove-watermark.py input.png output.png

Force dimension preservation:

python scripts/remove-watermark.py input.png output.png --method inpaint

Fast batch processing:

python scripts/batch-process.py ./input ./output --method aggressive

Disable auto-detection (force bottom-right):

python scripts/remove-watermark.py input.png output.png --method inpaint --no-detect

4. Verify Results

• Check output images for clean corners

• Verify no important content was cropped

• Confirm watermark fully removed

Command Reference

remove-watermark.py

python scripts/remove-watermark.py INPUT OUTPUT [OPTIONS]

Arguments: INPUT Input image path OUTPUT Output image path

Options: --method {crop|inpaint} Removal method (default: crop) --size INT Watermark size in pixels (default: 60)

batch-process.py

python scripts/batch-process.py INPUT_DIR OUTPUT_DIR [OPTIONS]

Arguments: INPUT_DIR Directory with images to process OUTPUT_DIR Directory for cleaned images

Options: --method {crop|inpaint} Removal method (default: crop) --pattern PATTERN File pattern to match (default: *.png) --size INT Watermark size in pixels (default: 60)

Examples

Example 1: Website Images

Scenario: User has 3 Google AI images for website, watermarks need removal

Batch process all PNG images

python scripts/batch-process.py
./public/images
./public/images-clean
--method crop
--pattern "*.png"

Output:

Found 3 images to process Input: ./public/images Output: ./public/images-clean Method: crop

[1/3] Processing: ad-design-bedroom.png ✅ Saved to: ad-design-bedroom.png [2/3] Processing: ad-design-kitchen.png ✅ Saved to: ad-design-kitchen.png [3/3] Processing: ad-designs-bathroom.png ✅ Saved to: ad-designs-bathroom.png

✅ Successfully processed: 3/3

Example 2: Preserve Exact Dimensions

Scenario: Client needs exact 1920x1080 image, can't crop

python scripts/remove-watermark.py
hero-image.png
hero-image-clean.png
--method inpaint

Example 3: Larger Watermark

Scenario: Watermark is bigger than usual (70px)

python scripts/batch-process.py
./images
./images-clean
--method crop
--size 70

Technical Details

See references/synthid-watermark.md for:

• SynthID watermark specifications

• Method comparison details

• Edge cases and considerations

• Legal/ethical guidelines

Dependencies

Required:

• Python 3.7+

• Pillow (PIL): pip install Pillow

• NumPy: pip install numpy

Optional (for ML inpainting):

• OpenCV: pip install opencv-python

Install all dependencies:

pip install Pillow numpy opencv-python

Note: The inpaint method requires OpenCV. Other methods work without it.

Tips

Performance:

• Crop method is 5-10x faster than inpaint

• For 100+ images, use crop method

Quality:

• Save with quality=95 to minimize compression

• PNG format preserves quality better than JPEG

Backup:

• Always keep original watermarked images

• Process copies, not originals

Testing:

• Test on one image before batch processing

• Verify watermark size with --size flag if needed