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An Overview of Computational Imaging in the Machine Vision Industry

Computational Imaging in Machine Vision Computational imaging (CI) is the digital image capture and processing techniques, combining computation and optical encoding, to capture images that are superior in quality or even impossible to capture with traditional imaging techniques. CI is widely used in photography, but it’s only now just making an entrance into the machine vision industry.

CI does not refer to the analysis of images – it’s inherent in the image capture process and requires the tight integration of a number of imaging components. Typically, these systems are proprietary and platform-specific because of the tight integration that’s required.

Although CI is relatively new to the machine vision industry, there are many potential benefits to end users and it can be deployed in a number of different ways.

The Benefits of Computational Imaging in Machine Vision

Machine vision users can benefit from CI, especially when higher quality imaging with less maintenance is desired. CI eliminates the need to adjust optics and lighting to get the perfect image. It also reduces the need for post-processing of images after they’re captured. Once a CI machine vision system is properly set up, the inherent computation in image capture processes delivers the exact image needed every time.

CI also allows for clearer images with more information than was previously possible with traditional imaging systems. Image capture can be adjusted to enhance an image without post-processing to more clearly highlight exactly the objects of image capture, improving the quality of machine vision which can lead to a higher overall quality of production.

What is Computational Imaging Used for in Machine Vision?

CI can be used in machine vision in a number of ways. Whether it’s for more depth information, better colors, more contrast, greater depth of field, or any other imaging characteristic, CI can help create innovative imaging solutions.

CI can be used in machine vision inspection applications, for example, to help separate the surface of an object from its background for more accurate detection of faults and defects – a highly productive quality for manufacturers to have.

While CI is still relatively new in machine vision applications, and much of the existing solutions are platform-specific, the potential of CI is great. Users of machine vision are always looking for higher performance systems, and CI can take imaging to the next level.

With the ability to capture images with information that’s impossible with many traditional machine vision systems, CI may become a widely adopted solution across the machine vision industry.

To learn more on this topic, take a deeper dive into CI by registering for our free webinar, “Sensing and Computational Imaging.”

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