Definition
In artificial
intelligence, image generation is a transformative technology that enables
machines to produce new images using either textual prompts or existing
visuals. These images are generated by models trained on vast datasets, with millions
of images and related text or metadata. Through this exposure, AI models learn
to understand patterns—shapes, colors, styles, and contexts, and use that
understanding to create new images from scratch [1].
AI image generation is usually powered by deep learning techniques like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs).
For example, imagine
a scenario where you are walking around a Nollywood film poster exhibition at
Terra Kulture in Lagos, where movie artwork seems to blend traditional African
storytelling with cinematic perfection. One poster catches your eye: a depiction
of a powerful masquerade dancer emerging from misty village square smoke,
staring intensely at the viewer, evoking the atmosphere of ancient Igbo
kingdoms through its dramatic lighting and what appears to be authentic raffia
costume details and wooden mask carvings. But the interesting thing here is
that these aren't photographs taken during actual film production but creations
by DALL-E, an AI image generator.
Now, this
example gives us a glimpse of a world where image generation and creating
visually rich content are at the forefront of AI's capabilities. Industries and
creatives are increasingly tapping into AI for image creation, making it
imperative to understand: How should one approach image generation through AI?
Origin
The origin of the modern resurgence of AI imaging began can be traced to the 2010s, fueled by breakthroughs in deep neural networks and datasets like ImageNet, developed by Fei-Fei Li, which enabled machines to surpass human capabilities in image recognition by 2015.
Deep learning
has revolutionized AI imaging, enabling innovative applications that spans
across fields such as medicine, biotech, art, and entertainment. Building on LeCun’s
late 1980s work with convolutional neural networks (CNNs)- networks designed to
process images similarly to how our brains do, the field has reached new
heights. Today’s systems, including Goodfellow’s 2014 GANs (Generative
Adversarial Networks), create hyper-realistic AI-generated images.
As AI imaging continues to evolve, it remains a testament to decades of innovation, collaboration, and curiosity in the pursuit of intelligent machines [3].
Context and Usage
Today, beginners
and professionals in diverse fields utilize AI image generation tools for
speedy creative work with quality output and bringing ideas to life efficiently.
Some of their applications are as follows:
- Game Asset Design: It speeds up the game development process by helping game developers create characters, backgrounds, and assets faster.
- Product Prototyping: Companies visualize product designs early, helping teams understand ideas before making physical models.
- Marketing Content: Marketers generate attention grabbing images for ads, social media, and campaigns, reducing production time and saving costs.
- Art & Concept Design: Artists and designers create new ideas and visuals quickly, experiment with styles and concepts, avoiding building from ground up.
- Educational Visualizations: Teachers and creators produce clear and engaging images, making complex concepts or topics simple and easy to understand [4].
Why it Matters
With just a few
clicks, you can use AI image generators to produce images that appear to have
been taken by a professional photographer. They offer benefits to content
creators, marketers and other creatives that go beyond time-saving and
cost-saving, to even create images of things that would never be found in
reality, turning your wildest ideas into something tangible.
For instance, you
can generate AI images of your latest design of women's ankara dresses and
African print clothing, showcasing them on models representing different ethnic
groups like Yoruba, Igbo, Hausa women. You can then use these images on your
online store or an e-commerce website instead of paying expensive Lagos models
and photographers, achieving a more professional look that attracts customers
shopping for wedding guest outfits and church attire.
Related AI
Transformation Techniques
- Image Denoising: Technique for removing noise and artifacts from images to improve clarity.
- Image Inpainting: AI technique for filling in missing or damaged parts of images realistically.
- 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
Dall-e is a good example of a real-life case study of image generation in practice. Developed by ChatGPT’s parent company, OpenAI, Dall-e is one of the most advanced image generation apps available. The latest version of it is powered by a diffusion model, with the ability of combining unrelated concepts and text into realistic, high-quality images [5].
Reference
- Payong, A., Mukherjee, S. (2025). Understanding AI Image Generation: Models, Tools, and Techniques.
- AltexSoft Editorial Team. (2023). AI Image Generation Explained: Techniques, Applications, and Limitations.
- Veronica. (2024). AI Image Generation: pros, cons and amazing tech for the future of humanity.
- Shalwa. (2025). AI Image Generation Explained: Technology Behind It.
- PlusAI. (2024). How does AI make images? Exploring AI image generators, their applications, and controversies.