How to Remove Unwanted Objects from Photos Using Apple Intelligence Clean Up in iOS 18

Capturing the perfect photograph on an iPhone often requires dealing with uncontrollable environmental distractions—such as background pedestrians strolling into the frame, stray power cables, reflections on windows, or photobombing pets. With the rollout of Apple Intelligence in iOS 18.1, the native Photos app incorporates a generative machine learning editing tool known as Clean Up. This feature enables iPhone owners to eliminate distracting background elements without third-party photo editing software.

Clean Up operates directly on-device using neural processing engines to detect background distractions automatically, isolate unwanted objects, and synthesize contextual textures that seamlessly match surrounding lighting, shadows, and textures.

System Requirements and Device Compatibility

Because Clean Up utilizes on-device Apple Intelligence diffusion models, it requires specific hardware capable of running local machine learning architectures:

  • Supported iPhone Models: iPhone 16, iPhone 16 Plus, iPhone 16 Pro, iPhone 16 Pro Max, iPhone 15 Pro, and iPhone 15 Pro Max.
  • Operating System: iOS 18.1 or later.
  • Apple Intelligence Activation: Apple Intelligence must be toggled on under Settings > Apple Intelligence & Siri.

Accessing the Clean Up Tool in the Photos App

The Clean Up interface is directly integrated into the primary editing suite of iOS:

  1. Open the Photos app on your compatible iPhone running iOS 18.1+.
  2. Select the picture containing the distraction you wish to remove. Clean Up functions on standard photos, Portrait shots, Live Photos, and archived pictures saved to your iCloud library.
  3. Tap the Edit button (the three slider controls icon) at the bottom of the display.
  4. In the editing toolbar, tap the Clean Up icon (represented by an eraser over an image frame).
  5. If opening Clean Up for the first time, your device may pause briefly while downloading the necessary local neural network assets.

Detecting and Removing Distractions

The Clean Up engine provides multiple interaction methods to erase background objects depending on their size and complexity:

  • Automated Object Detection: The machine learning model analyses the image composition and places subtle glowing or pulsing highlights around suspected distractions (such as stray tourists or background trash cans). Simply tap the glowing highlight, and Clean Up instantly erases the object while blending the background.
  • Circling Objects: If a specific distraction is not automatically highlighted, use your finger to draw a circle around the object. The neural engine detects the boundaries of the circled item, extracts it, and fills the void with synthesised pixels.
  • Brushing Over Small Artifacts: For complex or irregular items—such as tangled wires, blemishes, or skin reflections—lightly brush over the target area with your fingertip. You can pinch to zoom into the image for pixel-level precision before brushing.

Refining and Managing Edits

Clean Up operates non-destructively, preserving the underlying original image data throughout the process:

  1. If an automated removal generates an unnatural texture or imperfect blend, tap the Undo curved arrow at the top of the screen to revert the latest stroke.
  2. Press and hold the image preview to compare the edited rendering with the original photograph.
  3. Once satisfied with the composite result, tap Done in the upper-right corner to save the modifications.

Because the Photos framework is non-destructive, you can re-open an edited picture at any future time, tap Edit, and select Revert > Revert to Original to restore the unedited capture with all original pixels intact.

Mastering the Clean Up tool in iOS 18 provides iPhone users with a fast, private, and exceptionally capable photographic retouching workflow directly within the native operating system.

Get the best tech tips delivered straight to your inbox.

Join thousands of readers mastering Apple, Google, Microsoft, and Linux.