Lens Distortion Rectification using Triangulation based Interpolation
This addresses a common issue in image processing for applications requiring linear camera models, but it is incremental as it builds on existing calibration methods.
The paper tackles the problem of lens distortion rectification by proposing a method that directly uses the inverse distortion model, avoiding additional errors from approximating its inverse, and demonstrates good performance across a wide range of parameters.
Nonlinear lens distortion rectification is a common first step in image processing applications where the assumption of a linear camera model is essential. For rectifying the lens distortion, forward distortion model needs to be known. However, many self-calibration methods estimate the inverse distortion model. In the literature, the inverse of the estimated model is approximated for image rectification, which introduces additional error to the system. We propose a novel distortion rectification method that uses the inverse distortion model directly. The method starts by mapping the distorted pixels to the rectified image using the inverse distortion model. The resulting set of points with subpixel locations are triangulated. The pixel values of the rectified image are linearly interpolated based on this triangulation. The method is applicable to all camera calibration methods that estimate the inverse distortion model and performs well across a large range of parameters.