Dpi Upscaler Checker

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

Features

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

Use Cases

1. Academic paper figure DPI checking
2. Print image quality pre-inspection
3. Low-resolution material restoration
4. 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

| Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | --input | string | - | Yes | Input image path or folder | | --output | string | stdout | No | Output report path | | --target-dpi | int | 300 | No | Target DPI threshold |

Upscale Command

| Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | --input | string | - | Yes | Input image path or folder | | --output | string | - | Yes | Output path | | --scale | int | 2 | No | Scale factor (2/3/4) | | --min-dpi | int | - | No | Only process images below this DPI | | --denoise | int | 0 | No | Denoise level (0-3) | | --face-enhance | flag | false | No | Enable face enhancement |

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

1. Automatically saved as <original_filename>_upscaled.<extension>
2. Preserves original EXIF information
3. Sets DPI to 300

Dependencies

1. Python >= 3.8
2. Pillow >= 9.0.0
3. opencv-python >= 4.5.0
4. numpy >= 1.21.0
5. 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

1. Default use of Real-ESRGAN model
2. Support lightweight bicubic interpolation fallback
3. 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

| Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

Security Checklist

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

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

1. [ ] Successfully executes main functionality
2. [ ] Output meets quality standards
3. [ ] Handles edge cases gracefully
4. [ ] 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

1. Current Stage: Draft
2. Next Review Date: 2026-03-06
3. Known Issues: None
4. Planned Improvements:

- Performance optimization - Additional feature support