Simple Implementation of Lens Distortion Correction Using MATLAB

This article demonstrates a straightforward approach to implementing lens distortion correction using MATLAB, applicable to both CMOS and CCD imaging systems. The process begins by collecting sample images captured through distorted lenses. These images are imported into MATLAB using functions like imread() for further processing. The correction workflow utilizes MATLAB's Image Processing Toolbox, specifically the cameraCalibrator app or programmatic functions like estimateCameraParameters(). Prior to distortion correction, essential preprocessing steps include: - Noise reduction using filters such as medfilt2() or imgaussfilt() - Edge detection with algorithms like Canny (edge() function) - Corner detection using detectHarrisFeatures() or detectMinEigenFeatures() for calibration pattern recognition These preprocessing operations enhance the accuracy of distortion parameter estimation. The core correction involves calculating radial and tangential distortion coefficients through camera calibration, then applying the transformation using undistortImage() function. After correction, the processed images can be exported via imwrite() for use in other applications. This method provides a practical, code-efficient solution for image processing tasks, with key MATLAB functions including: - Camera calibration: estimateCameraParameters() - Distortion correction: undistortImage() - Feature detection: detectHarrisFeatures() The implementation requires minimal coding while delivering professional-grade distortion correction suitable for computer vision applications.