Removing unwanted objects from pictures with Cleanup.pictures

Ever been on vacation somewhere, and wanted to take a picture of something, only to be thwarted by the hordes of tourists? Typically for me it’s buildings of architectural interest, or wide-angle photos in towns. It’s quite a common occurrence, especially in places where tourists tend to congregate. There aren’t many choices – if you can come back at a quieter time that may be the best approach, but often you are at a place for a limited time-frame. So what to do?

Use software to remove the offending objects, or people. Now this type of algorithm designed to remove objects from an image has been around for about 20 years, known in the early years as digital inpainting, akin to the conservation process where damaged, deteriorating, or missing parts of an artwork are filled in to present a complete image. In its early forms digital inpainting algorithms worked well in scenes where the object to be removed was surrounded by fairly uniform background, or pattern. In complex scenes they often didn’t fair so well. So what about the newer generation of these algorithms?

There are many different types of picture cleaning software, some stand-alone such as the AI-powered IOS app Inpaint, others in the form of features in photo processing software such as Photoshop. One new-comer to the scene is web-based, open-source, Cleanup.pictures. It is incredibly easy to use. Upload a picture, choose the size of the brush tool, paint over the unwanted object with the brush tool, and voila! a new image, sans the offending object. Then you can just download the “cleaned” image. So how well does it work? Below are some experiments.

The first image is a vintage photograph of Paris, removing all the people from the streets. The results are actually quite exceptional.

The second image is a photograph taken in Glasgow, where the people and passing car have been erased.

The third image is from a trip to Norway, specifically the harbour in Bergen. This area always seems to have both people and boats, so it is hard to take clear pictures of the historical buildings.

The final image is a photograph taken by Prokudin-Gorskii Collection at the Library of Congress. The image is derived from a series of glass plates, and suffers from some of the original glass plates being broken, with missing pieces of glass. The result of cleaning up the image, actually has done a better job than I could ever have imagined.

The AI used in this algorithm is really good at what it does, like *really good*, and it is easy to use. You just keep cleaning up unwanted things until you are happy with the result. The downsides? It isn’t exactly perfect all the time. In regions to be removed where there are fine details you want to retain, they are often removed. Sometimes areas become “soft” because they have to be “created” because they were obscured by objects before – especially prevalent in edge detail. Some examples are shown below:

Creation of detail during inpainting

Loss of fine detail during inpainting

It only produces low-res images, with a maximum width of 720 pixels. You can upgrade to the Pro version to increase resolution (2K width). It would be interesting to see this algorithm produce large scale cleaned images. There is also the issue of uploading personal photos to a website, although they do make the point of saying that images are discarded once processed.

For those interested in the technology behind the inpainting, it is based on an algorithm known as large mask inpainting, developed by a group at Samsung, and associates [1]. The code can be obtained directly from github for those who really want to play with things.

  1. Suvorov, R., et al. Resolution-robust Large Mask Inpainting with Fourier Convolutions (2022)

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