Image Background Removal Best Practices

Guidelines and Best Practices #

The technology behind DROPBG is based on a combination of neural networks that were trained on a large dataset to create precise segmentation maps of salient objects and refine those maps to accurately separate the edges of the foreground from the background. These neural networks were also optimized to enable returning the result within seconds regardless of the image size.1

In the great majority of cases, these deep-learning algorithms return high-quality results. The following best practices can further increase the likelihood of good results:

  • Make sure there is enough background around the foreground object(s) on all sides.
  • Try to avoid large shadows. This is especially relevant if the background is a single color. For example, if the main subject is photographed against a plain light colored background, the object’s shadow might be classified as part of the foreground.
  • In some cases, hair or other very small or blurry parts on the edges of a foreground object might be blended with the background. For example, try to avoid hairs blowing in the wind.
  • In some cases, strong changes in contrast, such as a bright lamp or a bright sun included in the photo, can impact the results.
  • Light-colored reflections on a shiny object (e.g. shine from the camera flash) may be treated as part of the background and removed, especially if the color of the reflection is similar to the main background color.

However, as with any Deep Learning-based algorithm, even if you follow these guidelines, the results may not always be perfect. Additionally, keep in mind that the neural networks are continually training on new data and thus results may be different over time.

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