Definition
Inpainting is a technique that has to do with filling in missing regions of images which entails fixing the missing or damaged parts of an image, or removing an undesired object to create a complete image. With the capabilities of AI and the Stable Diffusion model, utilization of inpainting is not just restricted to restoring missing or flawed parts of the image, but can be used for rendering something entirely new in any part of an existing image [1]. It’s possible to recreate areas of an image that blend seamlessly with the original through analyzing the surrounding content and the existing details such as textures and patterns.
For example, imagine a scenario where you are documenting your sister's traditional Igbo wedding when suddenly a perfect moment appears, like the Ada (bride) performing the wine-carrying ceremony or masquerade dancers entering the village square for a few seconds. Without hesitation, you grab your phone and capture the beautiful cultural moment. Later you discover that a distracting object, like a plastic chair in the background or someone's modern handbag, is ruining the traditional aesthetic of your shot. With image inpainting, a technique for image manipulation from the field of deep learning, you could erase the distracting object just by highlighting it. Image Inpainting aims to cut out undesired parts of an image and fills up missing information with plausible content of patterns, colors, and textures that match the surrounding—removing that anachronistic plastic chair while preserving the authentic village compound background.
A sample of image inpainting [2].
Origin
Inpainting
methods originated back in year 2000 and are still under progress as more is
expected from it in the future. Early methods were Fast-Marching method and
Navier-Stokes method which combined the concepts of weighted sum representation
and partial differential equations, respectively.
2009 witnessed a
transformational breakthrough in the form of PatchMatch that iteratively
searches for a best match of hole patch in the image. The results were
astounding and smooth but the semantic meaning in the image was still missing. With
the era of Deep Learning gaining momentum, researchers moved towards Neural
Networks based Inpainting methods [3].
Context and
Usage
Image inpainting
is the task of restoring missing or damaged regions of an image using
information from the surrounding pixels. Image inpainting has many practical
applications such as the following:
- Graphic Design: you can repair or enhance digital artworks using custom inpainting maintaining consistency and creativity throughout projects
- E-commerce: This technology is used to remove distracting backgrounds or blemishes from product photos, creating clean, professional images that can lead to increased conversion rates.
- Fashion & Retail: It is used to retouch clothing details and refine product visuals to showcase designs optimally.
- Marketing & Digital Media: Inpainting enables creating visually compelling advertisements and social media content by removing imperfections and seamlessly integrating new elements.
- Real Estate: It is used to enhance property images by eliminating unwanted objects, producing more appealing and marketable listings [4].
Why it Matters
In the artificial
intelligence field, inpainting plays an important role by enabling the seamless
restoration and enhancement of visual content. This technology has strengthened
AI image processing capabilities, leading to innovative solutions for numerous
real-world problems.
Related AI
Transformation Techniques
- Image Denoising: Technique for removing noise and artifacts from images to improve clarity
- Image Generation: AI process of creating new images from text descriptions or other inputs
- Image-to-Image Translation: Converting images from one domain to another while preserving content structure
- Image Upscaling: AI technique for increasing image resolution while maintaining or enhancing quality
- Style Transfer: AI technique that applies the artistic style of one image to the content of another
In Practice
A real-life case study of image inpainting in practice can be seen in the case of Photoshop’s Generative Fill. Generative Fill in Photoshop use machine learning to analyse and predict how to fill missing areas more accurately and quickly than humans, without losing the image’s integrity during the restoration process. Unlike traditional methods, Generative Fill uses advanced AI to understand image context and fill in missing areas or remove elements in a way that’s indistinguishable from the original image [5].
Reference
- NightCafe Studio. (n.d). What is Inpainting in AI and how to use it?
- Ogawa, T., Haseyama, M. (2013). Image inpainting based on sparse representations with a perceptual metric.
- Tiwari, V. (2020). Image Inpainting and its evolution: A brief discussion…
- Deep-Image. (2025). Inpainting Image AI - Area generation with full user control.
- Adobe. (2025). Master the art of inpainting for flawless image restoration.