Camera lens distortion models software#

They compared statistical analyses of how different circle fitting methods contribute to accurate distortion parameter estimation. 18 – 26 Bukhari and Dailey 21 proposed a method for automatic radial distortion estimation based on the plumb-line approach. 6 In the past decades, many methods for radial distortion estimation have been proposed in research. Although the fact that the multiple views method does not require a special condition in the scene, for example, straight lines, making it have a wide range of applications, the disadvantage of this method is that it does require multiple images which are not available in many cases, to conduct the process. 17 It can process calibration after the image is imported with a lens distortion model that includes seven parameters which are sufficient for most kinds of cameras.

Camera lens distortion models software#

6, 7, 15, 16 For multiple views method, the most widely used offline calibration software is the toolbox provided by Jean-Yves Bouguet. Methods used for obtaining the parameters in the radial distortion function for correcting the distorted images can be divided roughly into two major categories: multiple views method 9 – 14 and single view method. 7 Radial distortion is the most significant type of distortion in today’s cameras. Radial distortion bends straight lines into circular arcs, 6 violating the main invariance preserved in the pinhole camera model, in which straight lines in the world map to straight lines in the image plane. 5, 6 It may appear as a barrel distortion or pincushion distortion. 1 – 4 In practice, for most lenses, the radial distortion component is predominant. Lens distortion is usually classified into three types: radial distortion, decentering distortion, and thin prism distortion. The experimental results on both synthetic and real images have demonstrated that the proposed method can robustly estimate and then remove image radial distortion with high accuracy. Hough entropy, as a measure, has achieved the quantitative evaluation of the estimated distortion based on the probability distribution in one-dimensional θ Hough space. This dramatically improves the convergence rate in the LM process to obtain the required parameters for correcting image radial distortion. Then “Taubin fit” is applied to obtain the initial guess of the arc’s parameters which works as the initial input to the LM iteration. Levenberg–Marquardt (LM) iterative nonlinear least squares method is adopted to calculate the arc’s parameters. A division model is applied, in which a straight line in the distorted image is treated as a circular arc. The approach is extremely useful in many applications, particularly those where human-made environments contain abundant lines. It works on a single image and does not require a special calibration. Abstract This paper presents an approach for estimating and then removing image radial distortion.