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The evolution of imaging: From DSLRs to computational cameras
A look at how imaging technology has evolved over the past 20 years, including the growing market of computational imaging, and how AI-driven solutions could reshape the future
I
maging technology has come a long way since its inception two centuries
ago. The advent of digital technology marked a turning point, allowing photons to be converted into electrons through optoelectronic sensors. This paved the way for computational imaging, gradually leading to the decline of many traditional standalone cameras, as miniature cameras became ubiquitous in smartphones. Computational imaging
branches into two distinct areas: computational photography and computer vision. Computational photography focuses on leveraging digital computation to capture and process images, while computer vision involves creating digital systems capable of interpreting and analysing visual data, much like the human visual system. These technologies have not only improved image quality but also unlocked new functionalities
including human and object recognition, 3D mapping, and feature extraction.
The rise of computational imaging According to industry intelligence firm Yole Group, as of 2022, the computational imaging market has soared to $68bn. One of the most significant trends in computational imaging is miniaturisation. The demand for ultra-compact cameras capable of delivering high-resolution, high-dynamic-range images is a driving force behind this trend. According to Emilie Viasnoff, Head of Optical Solutions at Synopsys: “Image quality now relies more than ever on high computing performance tied to miniaturised optics and sensors, rather than on standalone and bulky but aberration-free optics. This new trend for computational imaging can be used for computational photography and
computer vision. Miniaturised cameras that deliver high- resolution, high-dynamic-range images are a key driver of the computational imaging market. Because next-generation camera modules are ultra-miniaturised, their computing performance must restore the quality of the signal through post- processing.” To achieve this trend toward
miniaturisation, computational power is harnessed for post-processing, leading to the integration of artificial intelligence (AI) and image fusion as two prominent developments in the field. Gordon Cooper, Product
Manager for AI/ML Processor Products at Synopsys, explains: “The capability for high-performance computing opens the door to implement AI networks to improve image quality. Adding AI can improve low light performance, upscale image resolutions, recover image quality from cheaper
lenses, and more. This trend toward computational imaging and AI will disrupt the imaging pipeline and require newer, broader design tools in the future – which will allow companies to break out of the imaging pipeline design silos.”
Applications across industries Computational imaging has applications across various sectors, including consumer electronics, smart manufacturing, agriculture, healthcare, transportation, sports, retail, safety, and surveillance. In most cases, camera modules are integrated with companion chips as embedded systems. This integration necessitates a focus on critical factors such as power consumption, storage capacity, and latency.
An imaging system is a
complex ecosystem consisting of several intricate components and advanced software, and while these subparts work together seamlessly, they can be designed by different teams using disparate tools. The real challenge arises during assembly and calibration when test engineers must manually validate various aspects of the system. However, by taking a holistic, system-level view of the imaging pipeline and incorporating AI algorithms at multiple stages, optical and electronic engineers could enhance computational imaging systems.
Synopsys has a broad portfolio of solutions that can be used to design imaging systems 42 Electro Optics October 2023
Leveraging AI algorithms for enhanced imaging One advantage of breaking out of the design silos is the ability to harness AI algorithms across the entire imaging pipeline. This approach could alleviate hardware constraints and optimise performance. Says Viasnoff: “Today’s AI-enabled miniaturised imaging systems offer tremendous functionality with computationally improved contrast, colour, sharpness, depth of focus, high dynamic range, and high motion accuracy, close to high-end traditional digital cameras. As an example, in the Apple iPhone 14 Pro, the image quality results from the three main
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