MATLAB Implementation of Color Image Scaling Using Bilinear Interpolation Algorithm

This text describes a MATLAB implementation of color image scaling that utilizes the bilinear interpolation algorithm. This algorithm effectively handles image resizing operations by calculating new pixel values as weighted averages of the four nearest original pixels, resulting in superior detail preservation and smoothness during both enlargement and reduction of images. Through MATLAB's powerful image processing capabilities, we can perform pixel-level precision scaling operations. The implementation involves reading the color image using imread(), separating RGB channels, applying the interpolation algorithm to each channel independently, and reconstructing the final image using cat() function. This approach maintains image integrity while adapting to various display requirements without noticeable distortion. The method not only enhances visual quality but also serves as a fundamental technique in image processing applications. The bilinear interpolation algorithm works by computing intermediate pixel values through linear interpolation in both horizontal and vertical directions, making it particularly effective for preserving edge information and reducing jagged artifacts. This implementation provides a solid foundation for subsequent image analysis and processing tasks, offering practical value in computer vision and digital imaging applications. Key MATLAB functions utilized in this implementation include: - imread() for image input - size() for dimension calculation - meshgrid() for coordinate generation - interp2() for 2D interpolation operations - imshow() for result visualization The code structure ensures efficient handling of three-color channels while maintaining color balance throughout the scaling process.