Paint over it. Let the photo fill the hole.
A power cable across a product shot, a tourist in the corner of a temple photo, a price sticker on a box. You paint the thing, the tool rebuilds the area from the pixels around it, and the picture keeps its texture.
The AI Object Remover paints over an unwanted object, rebuilds the area behind it from the pixels around the selection, and blends the patch so no outline is left. The repair runs in your browser, so the photo is never uploaded.
How it works
Add the photo
Open the picture with the object, person or text to erase.
Paint over it
Cover the unwanted part; the area is rebuilt from what surrounds it.
Download the photo
Save the patched picture.
Only the dashed area is thrown away. Everything around it is evidence for the fill: colour, brightness and the small high-frequency grain that makes a wall look like a wall.
Three kinds of jobs, three brush habits
Cables, hairs, scratches, a strand of grass across a face. Use a small brush and paint just over the line, adding ten or twenty pixels of margin on each side. A tight mask on a thin line leaves a visible trace because the fill has almost no data to work with.
Paint the whole shape in one go, including its shadow. Shadows are the usual reason a removal still looks wrong: the object is gone but a grey patch on the floor remembers it. If the area is large, fill it in two halves so the algorithm always has known pixels nearby.
Paint the letters plus a couple of pixels around them. Straight edges are the hardest thing to rebuild, so if the sticker sits on a flat surface, switch the fill to smooth and keep the brush margin generous.
- check_circleThe whole object is inside the red paint
- check_circleIts drop shadow is painted too
- check_circleNo unpainted sliver on the far edge
- check_circleLarge areas are split into two passes
Backgrounds that heal well
Sky and cloud
Soft gradients and cloud are the easiest case: the fill only has to continue a slow change of colour.
Walls, floors, sand
Flat surfaces with grain come out convincingly, especially with texture matching switched on.
Grass and gravel
Random texture hides small errors, so a path or a cable through grass is a good candidate.
Brick and tiles
Regular patterns are risky: a course of bricks that stops or bends is visible immediately. Paint wider and fill in two passes.
Faces and hands
Avoid painting over people unless the area is small. Skin has no repeating pattern for the fill to copy.
Text on paper
Letters cannot be reconstructed. Removing a line inside a paragraph leaves a gap that a reader notices.
What does texture matching change?expand_more
The fill has two ingredients: a smooth colour field that comes from the surrounding area, and the fine high-frequency detail borrowed from the nearest known pixels. With texture matching on, both are used, so a wall keeps its grain. With it off, only the smooth field is used, which is cleaner on out-of-focus backgrounds and studio paper.
Why is there a faint smudge left?expand_more
Either a sliver of the object was outside the paint, or the pattern around the hole was interrupted. The usual fix is to paint slightly wider than before, including the smudge, and fill again. Three small passes beat one enormous one.
How big an area can be removed?expand_more
As a rule, smaller than a tenth of the picture. A small object on a large background fills convincingly; a whole person filling half the frame leaves a smooth region the eye reads as a blur.
Does the export keep the resolution?expand_more
Yes. Working happens at up to 2400 pixels on the long side and the download is a PNG at that size. Nothing is upscaled or downsized on the way out.
Object removal questions
Is the photo uploaded anywhere?expand_more
Can I remove a watermark from someone else's photo?expand_more
Does it work on a phone?expand_more
Can I undo a fill?expand_more
What format do I get?expand_more
Why does a removal look better on some photos than others?expand_more
Object Removal Without a Server: What the Fill Really Does
Removing something from a photograph is a reconstruction problem. The pixels under the object were never recorded, so the program has to invent them from the pixels that surround the hole. Every tool does that in one of two ways: it either learns from millions of other photographs what usually goes in such a place, or it continues the pattern immediately around the mask. The first needs a server and a model. The second is what a browser can do in a second, and it is remarkable how often it is enough.
The fill on this page is a diffusion. Starting from the rim of the mask, colour and brightness are averaged inward until the hole is covered. That produces a smooth field that continues a gradient: a sky becomes lighter toward the horizon exactly as it did beside the hole, a wooden floor keeps its tone, a shaded wall keeps its falloff. On its own, though, a diffusion looks like a smudge, because it has no grain.
The second ingredient: borrowed detail
A photograph is not just colour, it is also texture — the fine, high-frequency variation of a real surface. The tool separates the two: it keeps the smooth field, then samples the detail from the nearest known pixels and adds it back inside the mask. That is why a plain wall keeps its plaster and a patch of grass keeps its blades instead of turning into a blurred stain.
This borrowing is also the source of the failures. If the nearest known pixels are a different material than the area being filled, the texture is wrong: a brick line can continue across a window, a shadow can stop abruptly. The practical response is to keep masks small and to paint the object plus its shadow, so the borrowed detail comes from the same material on both sides.
Painting better masks
Most disappointing results come from the mask, not the algorithm. Three habits fix almost all of them. First, paint ten to twenty pixels beyond the visible edge of the object; the soft boundary of a lens blur extends further than the eye expects. Second, include the shadow — a removal that leaves a shadow looks wrong even when the object is gone. Third, if the area is large, do it in two halves: the first pass gives the second one more known pixels to work from.
When a desktop editor is the honest answer
Content-aware fill in a professional editor does the same job with more compute, and a retoucher can clone texture precisely where a diffusion guesses. If the hole covers a face, a page of text, a product logo or a high-contrast pattern like a tiled roof, use those tools. This one is built for the everyday case: a cable across a product shot, a sign in the corner of a landscape, a stranger at the edge of a group photo, dust on a scan.
A workflow that keeps you out of trouble
- Work on a copy. The original is the only undo that always works.
- Fill the biggest object first, then the small ones that end up next to its edge.
- Check the result at 100%, not zoomed out — errors hide in thumbnails and shout at print size.
- Export PNG, and keep a note of which areas were rebuilt in case the picture is used at a larger size later.
One last thing worth knowing: the fill runs entirely in the tab, so the picture never leaves your machine and nothing is logged. That makes the tool usable for documents, ID photos and client material that a cloud service would not be allowed to see — a practical advantage that has nothing to do with the algorithm and everything to do with where it runs.
Dust, scratches and old scans
Family photographs and book scans are the quiet success story of this kind of fill. A dust speck, a scratch or a crease is small, sits on a surface with an even texture, and therefore heals almost invisibly. Work at a large brush size for the whole scratch, not point by point: the mask should cover the damage plus a margin, and the texture matching will borrow the paper grain from both sides.
The limit arrives with anything printed underneath. A scratch across a face, a caption, or a printed halftone pattern leaves a smooth patch the eye can find. In those cases the honest answer is a clone tool in a proper editor, used sparingly, with the original kept untouched next to the result.
Fix the rest of the picture
fact_checkQuick facts
- Removing an object means inventing the pixels behind it: a plain wall is repaired perfectly, a crowd or a repeating pattern is not.
- A soft blend edge hides the seam of the patch, while a hard edge leaves a visible outline where the fill stops.
- The canvas specification defines toBlob and toDataURL for exporting the drawing, and the format and quality are chosen at export time.menu_bookWHATWG
- A canvas is limited by memory: iOS caps its total canvas area, which is why very large image operations fail on a phone before a desktop.menu_bookWHATWG
- JPEG splits the picture into 8×8 pixel blocks and stores a discrete cosine transform of each block, which is where its block-shaped artefacts come from.menu_bookITU-T
helpQuestions people ask
How do I remove an object from a photo?expand_more
Why does the removed area look smudged?expand_more
Can object removal change the rest of the photo?expand_more
hubWhat this page is about
- Canvasopen_in_new
The HTML element that gives a page a pixel buffer to draw on and export as PNG, JPEG or WebP.
- JPEGopen_in_new
A lossy still-image format standardised as ITU-T T.81 in 1992 and still the default format of most cameras and phones.
- ICC colour profileopen_in_new
A file that describes how the colours of an image map to a device, defined by the International Color Consortium.
library_booksOfficial sources cited on this page
Continue with the same file
Each of these opens what the tool above produces. Use “Use in another tool” under the result to carry the file over — it moves from tab to tab inside your browser, never to a server.
Switched-off tools are not listed here: only what works right now.