Image Skew Correction

This algorithm excels in image skew correction while demonstrating superior processing efficiency. The implementation employs robust feature detection methods including Canny edge detection followed by Hough line transformation to accurately identify skew angles. Key advantages include adaptive handling of various image types and dimensions, maintaining original image resolution and detail preservation through bilinear interpolation during rotation. The algorithm's architecture ensures minimal quality degradation by implementing careful coordinate transformation and pixel value recalculation. This makes it particularly valuable for document digitization systems, computer vision applications, and automated image processing pipelines where precision and speed are critical. Technical implementation typically involves: 1. Preprocessing with Gaussian blur for noise reduction 2. Edge detection using gradient-based operators 3. Angle calculation via Radon transform or projection profile analysis 4. Affine transformation with optimized rotation matrices This approach guarantees consistent performance across digital images and scanned photographs, making it suitable for integration in production environments requiring reliable image orientation correction.