3.2 Distortion Correction Technique: Image Correction Processing Code Implementation

Image distortion correction serves as a fundamental and crucial preprocessing technique in computer vision, primarily aimed at eliminating radial and tangential distortions introduced by camera lenses. Below is a detailed implementation approach: Distortion Model Establishment The process begins by capturing multiple images of a calibration pattern (such as a chessboard) to calculate the camera's intrinsic matrix and distortion coefficients. These parameters define the mathematical model of lens distortion, typically including k1/k2 coefficients for radial distortion and p1/p2 coefficients for tangential distortion. In code implementation, this is commonly achieved using OpenCV's camera calibration functions like `calibrateCamera()` which returns the essential calibration parameters. Correction Mapping Calculation Using OpenCV's `initUndistortRectifyMap` function, developers generate coordinate mapping tables that transform points from the original distorted image to the corrected image coordinates. This function takes the camera matrix and distortion coefficients as inputs and produces two mapping arrays (map_x and map_y) that define the transformation. The reverse mapping approach employed here prevents holes in the output image that would occur with forward mapping during interpolation. Real-time Remapping The `remap` function applies the precomputed mapping tables to rearrange pixel positions from the original image to their corrected locations. This function supports various interpolation methods, with bilinear interpolation being the most common choice for smooth results that eliminate aliasing artifacts in the corrected image. The implementation typically follows: `remap(src, dst, map_x, map_y, INTER_LINEAR)`. Edge Processing Optimization Since distortion correction often causes stretching effects at image edges, developers implement optimization techniques such as ROI cropping or edge padding (like black extension) to maintain valid visual areas. Advanced approaches incorporate adaptive scaling algorithms to preserve more original image content while minimizing distortion artifacts. This can involve calculating the optimal new camera matrix using `getOptimalNewCameraMatrix()` before generating the remapping tables. This technology finds widespread applications in autonomous driving systems, industrial inspection, and medical imaging. When combined with GPU acceleration through frameworks like CUDA, real-time correction becomes feasible. Note that different lenses require individual calibration, and edge correction effectiveness significantly impacts accuracy, particularly with wide-angle lenses where distortion is more pronounced.