AI technology is being used in almost every field today, from content creation to process automation. AI image processing algorithms are an interesting area that stands to change the way businesses work with visual information. In this guide, we’ll explore AI-based image processing and discuss how it works, and some common use-cases for the technology.
What is AI Image Processing and How Does It Work?
AI algorithms for image processing can be used in two broad areas: to read information from images or to enhance or modify them. Machine learning algorithms are being used for object recognition, processing documents, and quickly improving image quality.
AI image processing works using a process similar to LLMs but with training based on images rather than text. The process is simple:
- Data collection: The machine learning model must be trained on a large number of images related to the task. Depending on the purpose of the model, a business might train the AI on public datasets (such as collections of faces/people/scenery) or internally gathered images.
- Recognition and training: The first round of training involves the model identifying patterns in the data and receiving training in the form of data labelling and feedback.
- Feature extraction: Convolutional Neural Networks (CNNs) use the information from data labelling to identify features. At this stage, the model will be asked to extract relevant features from new images to determine whether the training was comprehensive enough. For example, a model might be asked to identify humans in an image or pick out specific facial features.
- Fine-tuning: The above process is repeated several times on a mixture of new and synthetic images to verify the model’s ability to identify features (or respond to prompts) from new and previously unseen images. By repeating the process, engineers can confirm the model’s ability to perform well in a variety of scenarios.
- Inference: New images are gradually introduced. At this stage, the model should be able to make inferences based on previous training data and accurately respond to any requests – for example, an AI image processing model intended to recognize animals should be able to identify a cat or a parrot if it’s been trained on sufficient images of those animals.
- Post-processing and visualization: Once a machine learning model has been deployed, it will require regular re-training with new data and refining based on user feedback.
How Does AI-Driven Image Processing Function?
AI-driven image processing uses computer vision libraries with pre-built image processing functions included. There are libraries available for the most popular programming languages, including Python, Java, and C++. Some useful libraries include:
- OpenCV: This open-source computer vision library can be utilized to compress, enhance, and extract data from images. It includes modules for machine learning, object detection, and image processing, and is well-documented.
- Scikit-learn: This Python library builds on NumPy, matplotlib and SciPy and helps developers process and classify images, and extract features.
- Detectron2: Object detection and image segmentation can be greatly simplified with the use of this next-generation library.
- Visualization Library: Unlike the previously mentioned libraries, Visualization Library is designed for C++. This middleware is based on OpenGL and can be used for the development of portable, high-performance applications.
For simple object recognition or processing, developers might use an existing AI model. More complex tasks may require a developer to build a bespoke AI model.
Extracting Nutritional Data from Visual Food Representations
Foundational AI Algorithms and Models Used in Image Processing
Some of the key algorithms used in image processing include:
- Support Vector Machines: This algorithm lends itself best to image classification tasks.
- Decision Trees: Both regression tasks and image classification tasks can be completed efficiently using decision trees.
- K-Nearest Neighbors: The KNN algorithm can be used to classify images based on feature similarity.
Popular models include:
- AlexNet: A fast, accurate model for image classification.
- VGGNet: This model is incredibly computationally intensive, but can classify images or extract data with a high degree of accuracy.
- ResNet: A versatile model suitable for object detection, segmentation, and image classification.
- YOLO: Useful for fast and accurate real-time object detection.
- Mask R-CNN: A highly accurate model for image segmentation.
How Can AI Be Used to Process Images in Practical Applications?
Image processing is being used in a variety of industries. For example:
Healthcare
Diagnostic tools can pre-screen scans to look for anomalies and pick up issues that might be missed by a human observer. If an anomaly is detected during an AI review of a scan, the image can be flagged to get a quicker review from a human specialist. AI can also be used as a basic diagnostic tool to provide a “first-line” diagnosis of diseases.
Security
Machine learning can look for suspicious objects or unusual behaviors and alert a human security guard for an in-person threat assessment. This helps support faster response times in emergencies.
Finance
AI tools for facial recognition and ID document processing can help streamline security checks for banking and finance applications.
Retail
Using machine learning to track customer behavior or even monitor stock levels on shelves can help retailers operate their stores more efficiently. Through object detection and motion tracking, retailers can learn a lot about the day-to-day behavior of their customers.
How Neural Networks Enable Advanced Digital Image Processing
Neural networks are computational models featuring several layers and connections inspired by the human brain. They work by receiving raw data as input and performing transformations on that data to extract features and patterns.
A neural network may have many layers, each of which carries out a different process and adds weight to the information gathered, allowing the model to “learn” and adjust its pattern recognition as it gets more input.
The final layer is the output layer, which produces the results of the computations. A result could be an image in response to a text prompt, or a classification if the model was fed an image and asked to identify the objects in it.
What is FastAI Image Segmentation?
Image segmentation is a form of computer vision where each pixel in an image is color-coded based on what it represents. This allows for a more powerful form of object detection than simply drawing a bounding box around an object. FastAI is a popular tool for image segmentation. This Python library offers powerful features for training models and working with CNNs, such as U-Ne,t for efficient image segmentation.
For example, when training a model for use in a self-driving car, the car will want to be able to identify roads, streets, other cars and pedestrians. Using image segmentation, an image can be color-coded so that the model can not only tell that there’s a corner coming up or another car nearby, but also where the edges and curves of that object are. This is done by first identifying each object and applying a mask to it, then breaking down the image into smaller and smaller parts to further categorize the object and refine its boundaries.
What is Stable Diffusion Image Enhancement?
Stable Diffusion is another popular tool for image processing. It’s typically used for image editing – for example, restoring missing parts or enhancing images. Stable Diffusion can:
- Remove noise and artifacts
- Upscale images
- Replace the background of an image
- Add objects to images
Stable Diffusion is available under a community license, and an optimized version of the model can run even on relatively modest hardware, although the full model will produce significantly better results.
Tools and Platforms for AI-Powered Image Processing
There are numerous AI tools available to suit all needs and budgets. Some popular tools include:
AI Image Generators:
These apps and tools offer features for image creation and enhancement.
AI Image Recognition
These apps and platforms may be useful for object recognition and image classification.
Open Source Tools and Libraries to Assist With AI Image Processing
The following computer vision libraries help developers build their own AI image processing or computer vision applications.
- OpenCV: A library with interfaces for various languages, OpenCV supports machine learning, image processing, and object detection.
- Pillow: A fork of the Python Imaging Library that assists with basic image processing tasks that may be required during pre-processing.
- MATLAB: A popular tool for solving mathematical problems, with some programmable features. MATLAB includes an image processing toolbox with workflows for image analysis, processing, and visualization.
How Can Python-Based AI Frameworks Streamline Image Analysis?
Python is a programming language aimed at data scientists that has a relatively user-friendly syntax and pre-built libraries covering a variety of common tasks. Some useful frameworks include:
- TensorFlow: An open-source framework created by Google to support machine learning and deep learning, including computer vision.
- PyTorch: A deep-learning framework originally developed by the Facebook AI Research Lab. PyTorch has interfaces for Python, C++, and Java and can be used for both computer vision and natural language processing.
- Keras 3: A deep learning framework with support for Tensorflow, PyTorch, and other backend frameworks. Keras 3 helps developers design, deploy, and train models for computer vision and image processing.
Python frameworks offer a high-level way to interface with deep learning models, freeing developers from reinventing the wheel.
Challenges and Limitations in AI Image Processing
Emerging technologies always have risks and challenges, and AI image processing is no exception. Developers are still learning how to use these technologies efficiently and avoid legal, ethical, and technical pitfalls.
What Technical Hurdles Exist in Image Processing with AI
Technical challenges exist at almost every stage of image processing, including:
- The sheer volume and variety of images required for training models.
- The effort required when annotating and tagging data sets.
- Maintaining data quality when training models.
- Normalizing images to remove noise, compression artifacts, and other distortions that humans might not notice, but could impact how the model “sees” the image.
- Issues with bias creeping into models due to the training data provided.
- The high computational power required for training deep neural networks.
- The cost of storage and data transfer associated with training models.
- The difficulty in optimizing models for speed and accuracy.
- The risk of overfitting when training models.
- The risk of models generalizing if trained on data that isn’t diverse enough.
- Protecting models against adversarial attacks.
- The need to make the decision-making process followed by models transparent and explainable
As new algorithms are developed and toolchains mature, it’s becoming easier to address many of these challenges.
Technical Issues With Image or Video Processing?
Pre-Processing Strategies for Optimizing AI Models
Pre-processing can significantly speed up training and improve the results generated by your AI model. Before using training data, it should be cleaned and normalized to ensure it’s as high-quality as possible. The process should involve normalizing features, handling missing data points, standardizing formats and detecting outliers.
Why Is Pre-Processing Crucial for Enhancing Image Processing with AI?
Pre-processing is a key step in ensuring your model is trained on high-quality data. This is important because inconsistent data can lead to unexpected results. If an image processing model is trained on images of varying sizes, resolutions, compression levels, and color spaces, it might incorrectly pick up on some of those variances as being “features”. Inconsistent training data may also cause the model to miss other meaningful characteristics.
Missing data, a small and biased data set, outliers, and inconsistencies in image formats can also cause other problems, such as weak generalizability or bias in the results.
What Common Techniques Improve AI-Based Image Processing?
Pre-processing can help reduce training issues. Some common techniques to improve the results produced by AI models include:
Resize and Crop
Any images used should be resized and cropped so they’re all the same size and aspect ratio. This will help avoid distortion and inconsistency in training and can also reduce the computational cost and resource usage of the model.
Smooth and Filter
Remove blur, noise, and unwanted artifacts and highlight the edges of objects using filtering and smoothing techniques. This helps the algorithm identify key features of the image.
Normalize Data
Image normalization refers to the practice of adjusting the image to a standard form so that the machine learning model receives consistent input. Before using an image, the pixel values should be scaled to a common range or adjusted by subtracting the mean and dividing by the standard deviation. PyTorch provides useful features for this purpose.
Augment and Transform
To reduce the risk of overfitting, consider augmenting and transforming images to increase diversity. Flipping or rotating images and adjusting their brightness and contrast gives the model exposure to more variations on the image, adding the kind of distortions and different angles that might be seen in real-life uploads.
Extract and Select Features
Speed up training by extracting and selecting features that are relevant to your AI model. By doing this as a pre-processing stage, you reduce the redundancy of your images and help the model find informative features.
Emerging Trends and Future Directions in AI Image Processing
Generative AI has already reached the “hobbyist” era, with enthusiasts able to run foundation models on their home machines. We’re now reaching the stage where AI image processing is maturing, and developers are focusing on explainable AI, security ,and sustainability, to create models that are ethical, unbiased, and respect privacy regulations.
What New Technologies Are Shaping the Future of AI Image Processing?
Some technologies to watch in the coming years include:
- Generative Adversarial Networks: GANs were introduced in 2014 and have grown in complexity and power in recent years. Putting two neural networks against each other in a competitive framework produces powerful models for image generation. For example, the ESRGAN model offers powerful image upscaling, making it useful for medical imaging.
- Reinforcement Learning: This subfield of machine learning is becoming increasingly important, especially for models that need to make real-time decisions. Reinforcement learning is already being used to develop autonomous robotic systems and self-driving cars, and is likely to see application in many other areas of AI.
Top 5 Use Cases for AI Image Processing Across Industries
AI Image processing has applications in numerous industries, including:
- Extracting Nutritional Data From Food: ISV uses machine learning to extract nutritional information from photographs of foods, to help users living with Type 1 diabetes track their macronutrient intake.
- Optical Character Recognition: Financial businesses use AI-driven OCR tools to digitally process photos of ID documents and other important records.
- Visualizing RF Signals: Radio frequency analysis experts at XYZ use AI tools to help users identify RF signals associated with malicious activities. AI models take complex signals and generate easy-to-understand visualizations.
- Marketing Images: Travel company Agoda uses Vertex AI to create images and videos of travel destinations for use in their marketing.
- Medical Applications: Fairtility uses Google Cloud AI to analyze embryo images and quickly identify the embryos with the highest potential for successful implantation.
