Image Scaling Using Bilinear Interpolation in MATLAB

Bilinear interpolation in MATLAB for image scaling operations. Bilinear interpolation is a widely used image processing technique that calculates weighted averages of neighboring pixels to resize images while maintaining visual quality. This method preserves image details and smoothness through a four-point interpolation algorithm where each new pixel value is computed based on the weighted contribution of the four closest original pixels. In MATLAB implementation, the bilinear interpolation algorithm typically involves these key steps: First, map target coordinates back to the source image space. Then, identify the four neighboring pixels surrounding the calculated source position. Finally, compute the weighted average using distance-based weighting factors in both horizontal and vertical directions. The process can be implemented using MATLAB's built-in functions like imresize with the 'bilinear' parameter option. For custom implementation, developers can create functions that handle coordinate mapping, boundary checking, and interpolation calculations. The core algorithm utilizes the formula: f(x,y) = (1-α)(1-β)f(i,j) + α(1-β)f(i+1,j) + (1-α)βf(i,j+1) + αβf(i+1,j+1) where α and β represent the fractional distances from the neighboring integer coordinates. This MATLAB-based approach effectively modifies image dimensions while minimizing artifacts like pixelation or blurring, making it suitable for various applications including computer vision, medical imaging, and digital photography where precise image scaling is required.