What Is Image Inpainting? (2026 Explainer)

The Core Definition

Image inpainting takes two inputs:

1. An image with some region you want changed
2. A mask — a black-and-white image marking which pixels to replace

The output: a new image where the masked region is replaced with content that blends naturally with the surrounding image.

Quick Facts

What People Use Inpainting For

• Watermark removal — erasing logos, text overlays, date stamps
• Old photo restoration — filling in scratches, water stains, tape marks
• Object removal — removing photobombers, tourists, unwanted items in product photos
• Generative fill — extending image canvas (Photoshop's "outpainting")
• Face / skin retouching — removing blemishes, wrinkles, scars
• Restoring damaged scans — torn corners, missing pieces of old photos

Three Generations of Inpainting

1. Texture Synthesis (Pre-2010)

Sample patches from elsewhere in the image and stitch them in. Photoshop's "Content-Aware Fill" (introduced 2010) is the polished version. Strengths: predictable, fast. Weaknesses: visible repetition on textured backgrounds, fails on faces or unique content.

2. Convolutional Networks / LaMa (2018-2022)

Deep neural networks (LaMa, EdgeConnect, Partial Convolution) trained on millions of images. They learn what realistic content looks like rather than copy-pasting patches. Strengths: handles textures well, fast. Weaknesses: large masks (>30%) and faces still produce blur or weirdness.

3. Generative Diffusion (2022-Now)

Diffusion models (Stable Diffusion Inpainting, FLUX Fill, Adobe Firefly) can take optional text prompts and synthesize entirely new content. Strengths: handles huge masks, can inpaint faces and complex objects. Weaknesses: slower, more expensive, sometimes "hallucinates" weird details that don't match the rest of the image.

The Hard Limit Nobody Tells You

Inpainting works by inferring missing pixels from surrounding context. If the surroundings have no information about what was originally there, the AI cannot recover it — it can only invent something plausible.

This is why:

• Removing a small corner watermark over plain sky → near-perfect
• Removing a watermark over a face → AI invents a different face
• Removing 50% of an image → AI generates totally new content (may not match original intent)

For small marks (under ~15% of image area), inpainting is excellent. For large damaged regions, results vary.

Try It

The fastest way to understand inpainting is to use it. BigImg's free inpainting tool uses LaMa and works in your browser — draw a box over what you want removed, AI fills it in 10-20 seconds.

FAQ