Fisheye Lens Correction

This article addresses a situation where the implementation produces suboptimal results. While we present a brief overview here, deeper investigation into the underlying issues is necessary. To fully comprehend the performance limitations, we must examine various influencing factors such as hardware capabilities, potential software deficiencies, and environmental variables. The fisheye correction algorithm typically involves coordinate transformation using mathematical models like the equidistant or stereographic projection, followed by interpolation methods (e.g., bilinear or bicubic) for pixel remapping. Performance bottlenecks may arise from inefficient interpolation implementations or improper parameter calibration in the distortion model. Through systematic testing and experimentation - including evaluating different interpolation algorithms and optimizing OpenCV functions like cv2.remap() - we can identify root causes and develop enhanced solutions. This ensures effective problem resolution and maintains optimal software performance for real-time image processing applications.