Computational Imaging

Computational Imaging (CI) in Machine Vision is basically the ability to use multiple separately-acquired images, each created with a particular lighting or imaging technique, to create a single resulting “optimized” image that is based on processing the individual images to end up with a single computed composite image.

The key principles of Computational Imaging are:

  • Computation is inherent in the image formation process
  • Combines special optics and/or lighting, along with image processing, during the image capture process
  • Typically involves capturing a sequence of images using a different illumination technique, wavelength, or optics for each frame
  • Covers a wide variety of techniques, all designed to output more useful composite images or composite images with unique characteristics


Some of the typical functions that can be accomplished with applications that previously would have been difficult include:

Photometric Stereo (PMS)

Generate edge and texture images using shapes resulting from light shading. Photometric stereo allows the user to separate the shape of an object from its 2D texture. It works by firing segmented light arrays from multiple angles and processing the resulting shadows utilizing “shape from shading.”

This can make visually noisy or highly reflective surfaces easier to inspect. This capability is rapidly becoming popular in the machine vision market. Numerous machine vision suppliers are now offering photometric stereo tools that control sequential image acquisition and image processing within their operating systems or smart cameras.

However, you still need lighting! As an example, a basic PMS system may utilize a ring light with four individually-controllable 90-degree quadrants to cast a directional shadow around the raised features on an object as seen in the images of the tire sidewall below.

The ring light quadrants were fired in sequence to create the East, South, North, and West images shown below:

Computational Tire

Images of the tire sidewall character shapes are then created by combining these images in software, as shown below. The PMS routine removes the visual noise and leaves only the features of interest, in this case the tire size graphics.

Ultra-Resolution Color (URC)

Create higher-resolution color images with no interpolation artifacts. By using a higher-resolution monochrome camera and acquiring separate full-resolution monochrome images using red, green, blue lighting, a full-resolution composite image with improved color fidelity and minimal color noise is computed. Because there is no color filter used in the camera or color interpolation artifacts within the computed image, the image is enhanced for manual interpretation and archiving (medical testing). Plus, this technique will provide a higher-resolution per color channel than a 3-chip color camera.

The technique of multiple image acquisitions also applies to using any combination of LED wavelengths to visualize features-of-interest, including UV and IR lighting.

Graphic Courtesy of CCS America

Bright Field/Dark Field

Combine the advantages of two well-known lighting techniques.
Bright field and dark field are two common methods of illumination for machine vision inspection. Normally, they are used independently, as most samples image best using one method or the other. But what if your sample contains some features that can only be seen with bright field, and other features that can only be seen with dark field?

Multi-shot imaging solves this problem through the use of a combined bright field/dark field illumination technique. The bright field image is combined with the dark field image to generate an output image which contains the features or defects found in both input images.

Bright Field Dark Field
Graphic Courtesy of CCS America