Inpainting: The AI Technique Transforming Image Editing

With the rise of AI tools, image editing has become more accessible and sophisticated than ever before. Inpainting is one such AI technique that has gained popularity among image editors and artists alike. In this blog post, I will explore what inpainting is, its use cases, advantages and how to integrate it into a workflow.

What is Inpainting?

Inpainting is a process of filling in missing or damaged parts of an image using AI algorithms. It can also be used to generate alternative visuals or even create something totally new. It is a form of image restoration that involves extrapolating the pixels around a part to create a seamless and natural-looking replacement. Inpainting can be used to remove unwanted objects or blemishes from an image, or to reconstruct lost or damaged parts of a photograph for example.

Transforming and Editing: Fashion Photography

In the carousel on this blogpost, I will show you a typical inpainting workflow for generating and transforming portrait and fashion photography.

Use Cases of Inpainting

Inpainting has a wide range of applications in various industries, including entertainment, healthcare, and e-commerce. Some of the use cases of inpainting are:

Image Restoration:

Inpainting can be used to restore old or damaged photographs by filling in missing or damaged parts of the image. This is particularly useful for preserving historical images and artworks.

Object Removal:

Inpainting can be used to remove unwanted objects or people from an image, creating a cleaner and more visually appealing composition.

Video Editing:

Inpainting can be used to remove unwanted objects or blemishes from a video, creating a smoother and more seamless playback experience.

Product Photography:

Inpainting can be used to remove distracting elements from product photographs, creating a cleaner and more professional appearance.

Inpainting is a powerful tool that can save time and effort in image processing workflows.

Advantages of Inpainting

Inpainting offers several advantages over traditional image editing techniques:

Speed and Efficiency:
Inpainting algorithms can process images much faster than manual editing, saving time and effort.

Consistency:
Inpainting algorithms produce consistent results across multiple images, ensuring a uniform look and feel.

Accuracy:
Inpainting algorithms can accurately predict missing or damaged parts of an image, creating a natural-looking replacement that blends seamlessly with the surrounding pixels.

Flexibility:
Inpainting algorithms can be customized to suit different image types and styles, allowing for greater creative freedom.

Using inpainting on an existing product to create variable design elements and endless inspiration.

Partially inpainting to supercharge the background of a product shoot.

Take even more control over the output when uploading your own created mask.

Inpainting: A Crucial Workflow Step

AI-based inpainting is a technique used to fill in missing parts of an image or video using artificial intelligence. This technique can be incorporated into a workflow by using specialized software or programming libraries, such as OpenCV or TensorFlow, which provide pre-trained models for inpainting. Tangible use cases for inpainting include restoring damaged photographs, removing unwanted objects from images, and enhancing low-resolution images. Inpainting is particularly useful in the pre-processing stage of computer vision pipelines, where it can help improve the quality of input images for downstream tasks such as object detection or facial recognition.