Image Geometric Correction Algorithms

Image geometric correction algorithms primarily address distortions caused by lens imperfections, shooting angles, or object shapes. The segmented correction method proves particularly effective for complex distortion scenarios by dividing the image into multiple local regions for differentiated processing, offering superior adaptability compared to global correction approaches.

The core methodology involves partitioning the image into grid-like blocks, where specific control points (such as checkerboard corners) are used to compute mapping relationships between ideal coordinates and actual coordinates. Local geometric transformation models are established using thin-plate spline interpolation or polynomial transformations. For handling large perspective distortions, perspective transformation matrices can be integrated with the partitioning strategy, ensuring corrected images maintain local details while conforming to overall perspective principles.

This approach's advantage lies in handling non-uniform distortions, such as simultaneous barrel distortion from lenses and trapezoidal distortion from pitch-angle shooting. Through segmented parameter optimization, it overcomes limitations of single-model corrections. Typical applications include document scan rectification, street view image stitching, and curved surface imaging compensation in industrial inspection systems.

Implementation Notes: - Grid partitioning can be implemented using OpenCV's grid detection functions - Control point detection leverages corner detection algorithms like Harris or Shi-Tomasi - Thin-plate spline interpolation requires solving linear systems for deformation fields - Perspective transformations utilize homography matrices computed through point correspondences - Parameter optimization often employs least-squares fitting with regularization terms