Lens Distortion Rectification using Triangulation based Interpolation

TL;DR

This paper introduces a novel lens distortion rectification method that directly utilizes the inverse distortion model, employing triangulation and linear interpolation for improved accuracy in image correction.

Contribution

The proposed approach uniquely uses the inverse distortion model directly and applies triangulation with linear interpolation, enhancing rectification accuracy across various calibration methods.

Findings

Effective across a wide range of parameters

Reduces error compared to inverse approximation methods

Applicable to all inverse distortion model estimations

Abstract

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.