Introduction
If you have been following our blog, you most likely already know that artificial intelligence (AI) and machine learning (ML) technology are finding their path into every aspect of our lives. If you love taking pictures or videos, you have probably dabbled with digital photography filters and other enhancement tools for color correction or simply to improve image quality.
So, it probably comes as no surprise that AI is becoming an integral part of photo and video editing. What started as simple enhancements has developed into a sizable suite of video editing software. But what about older blurred images and videos? Paper images whose negatives have long been lost may become damaged and cracked. If they are the only copy left of the image, coveted memories may be lost. And what about that awkward corrupted image or pixelated image file? Can AI help?
Can AI Be Used to Restore Photos and Movies?
The simple answer is yes, artificial intelligence and machine learning can help you restore your precious photos and movies. Whether you are looking to enhance a blurred image, rescue a corrupted image or fix a pixelated image, AI can contribute to all of these. You do not need to be a technology expert either to take advantage of these powerful, yet simple video editing software tools.
For the uninitiated, AI is a branch of computer science that builds computers that perform tasks that would normally require human intelligence. AI mimics behaviors that we normally associate with humans, such as learning, to achieve certain tasks faster and often more precisely than humans could.
Machine learning is a subfield of AI that refers to algorithms able to learn from the results of their own calculations without further human intervention. Both technologies have become closely associated with processing vast quantities of data and deriving models of future behavior or outcomes based on their calculations. As computing power has become stronger, the abilities and applications of AI and ML have grown, too.
Also Read: Artificial intelligence and image editing.
How this AI Image Restoration Model Works
But let us get back to image enhancement. Imagine traditional, analog restoration of invaluable but damaged paintings. The restorator analyses the type of paint and the exact combination of colors used in the areas surrounding the damaged part. Then, they create the closest possible approximation to cover the damage and restore the painting. Ideally, the result will have viewers think the painting was never damaged.
In this brief, human example, we have some of the key components of using AI for photo and video restoration:
- Data analysis
- Learning from available information
- Recreating the missing information
AI-based video enhancement software and photo editing software are based on similar principles. Photo restoration tools start by identifying the damaged areas of the image. Your photo may have fractures from having been folded, it may have become scuffed, and some images even have holes. With the defects identified and highlighted, the software goes to work.
AI-based restoration and AI-based video enhancement software work by looking at the pixels surrounding the damaged part and deriving suitable pixels to fill in the gaps. This is the part that requires training for the algorithms used. If you were to program a brand-new AI or ML tool, the first few examples would rarely be perfect. However, with time, the algorithm learns from accepted versus rejected jobs and gets better. If you were working with black and white images originally, you may finish off by coloring the photo.
Granted, real-life restoration may not be as simple as it sounds, but it is certainly a field that some of the biggest technology companies are working on. Household names like Adobe Photoshop and Google Photos are just two examples. Photoshop’s clone stamp tool has long been a favorite with picture editors looking to improve a corrupted image or blurry images. But using those established tools can be time-consuming, with adjustments under image requiring users to deal with layers.