Calculating Image Modulation Transfer Function (MTF) with Algorithm Implementation

This text discusses the significance of calculating the Image Modulation Transfer Function (MTF), which serves as a critical tool for improving image sharpness. The implementation typically involves edge detection algorithms (like Sobel or Canny operators) to identify sharp transitions, followed by Fourier Transform analysis to evaluate frequency response. Beyond standard photographic images, the methodology can be effectively applied to various image types including satellite imagery. By computing MTF values, we gain quantitative insights into the clarity of details and edges within images, enabling targeted quality enhancement measures through techniques like deconvolution or sharpening filters. The process generally includes key steps: extracting edge spread function (ESF), deriving line spread function (LSF), and calculating frequency response via Fast Fourier Transform (FFT). Consequently, MTF stands as an essential metric in digital image processing, contributing significantly to superior visual outcomes in applications ranging from medical imaging to remote sensing.