Demand for AI capabilities in a variety of products is growing, and nowhere is this more evident than in computational imaging. The ubiquity of cameras integrated into smartphones (and delivery of high-quality images) has paved the way for cameras to be included in everything from doorbells to medical devices. There are dual branches in computational imaging:
- Computational photography, which involves the capture and processing of images using digital computation
- And computer vision, which involves the creation of digital systems that can process, analyze, and make sense of visual data as humans can
AI-driven computer vision technology is proving to be essential for delivering accurate, real-time, high-resolution imagery. Traditionally, convolutional neural networks (CNNs) were the dominant algorithm in computer vision applications. Neural networks have been commonly used for object detections and are now playing an important role in image quality improvement, taking over tasks that were once done by digital signal processors. For example, because they can embed knowledge of what a good image should look like, neural networks can upscale video streams to 4x the resolution via super resolution networks, and they can be used to reduce noise and enhance low light performance. Features such as blur reduction, high dynamic range, and wide dynamic range are also within the realm of AI-driven computer vision capabilities.
Now, AI transformers—originally developed for natural language processing such as translation and question answering—are emerging as the highest accuracy option. Transformers, based on a self-attention mechanism, are better at learning complex patterns for accurate object detection than CNNs, and therefore can better understand context. Used together with CNNs, the combination of the two deep-learning models can significantly enhance computer vision and image processing accuracy.