Posts Tagged ‘Image distortion’

How to solve the Image Distortion Problem

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      Theoretically, it is possible to define lens which will not introduce distortions. In practice, however, no lens is perfect. This is mainly due to manufacturing factors; it is much easier to make a “spherical” lens than to make a more mathematically ideal “parabolic” lens. It is also difficult to mechanically align the lens and imager exactly. Here we describe the two main lens distortions and how to model them. Radial distortions arise as a result of the shape of lens, whereas tangential distortions arise from the assembly process of the camera as a whole.

      We start with the radial distortion. The lenses of real cameras often noticeably distort the location of pixels near the edges of the imager. This bulging phenomenon is the source of the “barrel” or “fish-eye” effect. Figure1 gives some intuition as to why radial distortion occurs. With some lenses, rays farther from the center of the lens are bent more than those closer in. A typical inexpensive lens is, in effect, stronger than it ought to be as you get farther from the center. Barrel distortion is particularly noticeable in cheap web cameras but less apparent in high-end cameras, where a lot of effort is put into fancy lens systems that minimize radial distortion.

Figure 1.

        For radial distortions, the distortion is 0 at the (optical) center of the imager and increases as we move toward the periphery. In practice, this distortion is small and can be characterized by the first few terms of a Taylor series expansion around r = 0. For cheaper web cameras, we generally use the first two such terms; the first of which is conventionally called k1 and the second k2. For highly distorted cameras such as fish-eye lenses we can use a third radial distortion term k3.

In general, the radial location of a point on the image will be rescaled according to the following equations:

Here, (x, y) is the original location (on the imager) of the distorted point and (xcorrected, ycorrected) is the new location as a result of the correction.

The second largest common distortion is known as tangential distortion. This distortion is due to manufacturing defects resulting from the lens not being exactly parallel to the imaging plane.

Tangential distortion is minimally characterized by two additional parameters, p1 and p2, such that:

Thus, in total five (or six in some cases) distortion coefficients are going to be required.

Distortion example

      To ilustrate these theoretical points, I am going to show a couple of images taken by the same camera. The first of these images will show the picture with the distortion effect, whereas the second one will show the result of applying “undistortion” functions.

Figure 2. The image on the left shows a distorted image, while the right image shows an image in which the distortion has been corrected through the methods explained.