simple AI web application


Image inpainting is the process of filling regions of interest in images for generating a complete image from either a damaged, a deteriorating image or after removing an unwanted object. Recently deep neural networks have shown promising results for this challenging image processing task. Here, in saiwa we propose a two-staged generative deep image inpainting method called DeepFill v2. This method is capable of filling large and multiple areas of image without usual boundary artifacts, distorted structures and blurry textures inconsistent with surrounding areas that we observe in other deep networks. If you are interested for more technical information and the details of how DeepFill v2 works, please read the corresponding white paper. For simply define missing patches or unwanted objects, this service provides a handy tool that you may define masks with arbitrary shape and size overlaying on your own image of interest. Please “Generated mask online” option to try this tool. Additionally, you may upload your ready binary mask as well.

saiwa image inpainting service benefits from the following advantages and features:


  • Filling multiple regions simultaneously
  • Handling user-defined irregular masks
  • Employing distant special features for estimating local unknown regions
  • Eliminating boundary artifacts, distorted structures and blurry textures inconsistent with surrounding regions
  • Online mask generation
  • Exporting and archiving the results in user cloud space or locally
  • Service customization by saiwa© team using “Request for customization” option
  • Preview and download the resulting images or generated masksImage inpainting has a wide range of applications, including:1. Image re-composition
    2. Photo editing
    3. Image-based rendering
    4. Computational photography
    5. Mosaic removal
    6. Image restoration to reverse, repair, or mitigate deterioration, like cracks, scratches or dusts
    7. Removing red-eye
    8. Removing the stamped date from photographs


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