CheapRetouch

A privacy-first iOS photo editor for removing unwanted elements from your photos — powered by on-device machine learning.

Features

🧑 Person Removal

Tap on any person in your photo to instantly remove them. The app uses Apple's Vision framework to generate precise segmentation masks, then fills the removed area seamlessly.

📦 Object Removal

Remove unwanted foreground objects with a single tap. When automatic detection isn't possible, use the smart brush tool with edge-aware refinement for manual selection.

⚡ Wire & Line Removal

Easily remove power lines, cables, and other thin linear objects. The app detects contours and automatically selects wire-like shapes, or you can trace them manually with the line brush.

How It Works

CheapRetouch combines Apple's Vision framework for intelligent object detection with an AI-powered inpainting engine:

Object Detection (Vision Framework)

VNGenerateForegroundInstanceMaskRequest — Generates pixel-accurate masks for people and salient foreground objects
VNDetectContoursRequest — Detects edges and contours for wire/line detection
Tap-based selection — Simply tap on what you want to remove

AI-Powered Inpainting (LaMa Model)

The app uses LaMa (Large Mask Inpainting), a state-of-the-art deep learning model optimized for removing objects from images:

Model: LaMaFP16_512.mlpackage — A Core ML-optimized neural network running entirely on-device
Architecture: Fourier convolutions that capture both local textures and global image structure
Processing: Runs on the Neural Engine (ANE) for fast, efficient inference
Quality: Produces natural-looking results even for large masked areas

Technical Details:

Input resolution: 512×512 pixels (automatically crops and scales around masked regions)
Quantization: FP16 for optimal balance of quality and performance
Fallback: Metal-accelerated exemplar-based inpainting when needed

Processing Pipeline

1. User taps object → Vision generates mask
2. Mask is dilated and feathered for smooth edges
3. Region is cropped and scaled to 512×512
4. LaMa model inpaints the masked area
5. Result is composited back into original image

Privacy

🔒 100% On-Device Processing

No photos leave your device
No cloud services or network calls
No analytics or telemetry
Photo library access via secure PHPicker

Technical Stack

Component	Technology
UI	SwiftUI + UIKit
Object Detection	Vision Framework
ML Inference	Core ML (Neural Engine)
GPU Processing	Metal
Image Pipeline	Core Image
Fallback Processing	Accelerate/vImage

Requirements

iOS 17.0 or later
iPhone or iPad with A14 chip or newer (for optimal Neural Engine performance)

Performance

Operation	Target Time	Device
Preview inpaint	< 300ms	iPhone 12+
Full resolution (12MP)	< 4 seconds	iPhone 12+
Full resolution (48MP)	< 12 seconds	iPhone 15 Pro+

Non-Destructive Editing

All edits are stored as an operation stack — your original photos are never modified. Full undo/redo support included.

License

MIT License — see LICENSE for details.

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