Implementing Image Rotation (Nearest Neighbor and Bilinear Methods)

In MATLAB, image rotation can be implemented using two primary interpolation methods: the nearest neighbor method and the bilinear method. The nearest neighbor approach is a straightforward technique where each target pixel's intensity value is determined by selecting the value from the nearest original pixel in the source image. This method involves simple coordinate mapping using the round() function to find the closest pixel coordinates, making it computationally efficient but potentially resulting in jagged edges.

The bilinear method provides higher accuracy by calculating target pixel values through weighted averaging of the four surrounding original pixels. This algorithm involves two linear interpolations - first along the x-axis between adjacent pixels, then along the y-axis between the resulting values. MATLAB's imrotate() function inherently supports both methods through its 'nearest' and 'bilinear' interpolation parameters. When implementing manually, the bilinear method requires handling fractional coordinates and implementing proper boundary checks to avoid index errors.

Both methods allow flexible image rotation with trade-offs between computational efficiency and output quality. The nearest neighbor method is suitable for real-time applications where speed is prioritized, while the bilinear method is preferred for quality-sensitive applications requiring smoother rotational results. Developers can choose the appropriate method based on specific requirements by setting the interpolation parameter in imrotate() or implementing custom rotation algorithms with proper coordinate transformation matrices.