DPI Upscaler & Checker

Check if images meet 300 DPI printing standards, and intelligently restore blurry low-resolution images using AI super-resolution technology.

Features

• DPI Detection: Read and verify image DPI information
• Intelligent Analysis: Calculate actual print size and pixel density
• Super-Resolution Restoration: Use Real-ESRGAN algorithm to enhance image clarity
• Batch Processing: Support single image and batch folder processing
• Format Support: JPG, PNG, TIFF, BMP, WebP

Use Cases

• Academic paper figure DPI checking
• Print image quality pre-inspection
• Low-resolution material restoration
• Document scan enhancement

Usage

Check Single Image DPI

python scripts/main.py check --input image.jpg

Batch Check Folder

python scripts/main.py check --input ./images/ --output report.json

Super-Resolution Restoration

python scripts/main.py upscale --input image.jpg --output upscaled.jpg --scale 4

Batch Fix Low DPI Images

python scripts/main.py upscale --input ./images/ --output ./output/ --min-dpi 300 --scale 2

Parameters

Check Command

Upscale Command

Output Description

DPI Check Report

{
  "file": "image.jpg",
  "dpi": [72, 72],
  "width_px": 1920,
  "height_px": 1080,
  "print_width_cm": 67.7,
  "print_height_cm": 38.1,
  "meets_300dpi": false,
  "recommended_scale": 4.17
}

Restored Image

• Automatically saved as _upscaled.
• Preserves original EXIF information
• Sets DPI to 300

Dependencies

• Python >= 3.8
• Pillow >= 9.0.0
• opencv-python >= 4.5.0
• numpy >= 1.21.0
• realesrgan (optional, for best results)

Algorithm Description

DPI Calculation

Actual DPI = Pixel dimensions / Physical dimensions
Print size (cm) = Pixel count / DPI * 2.54

Super-Resolution

• Default use of Real-ESRGAN model
• Support lightweight bicubic interpolation fallback
• Intelligent model selection (general/anime/face)

Notes

1. Input image DPI information may be inaccurate; actual pixel calculation shall prevail
2. Super-resolution cannot create non-existent information; extremely blurry images have limited improvement
3. Large file processing requires more memory
4. GPU acceleration requires CUDA environment (optional)

Risk Assessment

Security Checklist

• [ ] No hardcoded credentials or API keys
• [ ] No unauthorized file system access (../)
• [ ] Output does not expose sensitive information
• [ ] Prompt injection protections in place
• [ ] Input file paths validated (no ../ traversal)
• [ ] Output directory restricted to workspace
• [ ] Script execution in sandboxed environment
• [ ] Error messages sanitized (no stack traces exposed)
• [ ] Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
• Performance optimization
• Additional feature support