Restoration of Motion-Blurred Images
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This article presents a motion-blurred image restoration method that utilizes cepstrum analysis to estimate blur parameters. The implementation involves calculating the logarithmic power spectrum of blurred images and identifying characteristic patterns to determine blur length and angle. We provide detailed MATLAB/Python code examples demonstrating key steps including Fast Fourier Transform (FFT) operations, cepstrum computation, and Wiener filter-based restoration. The code structure features modular functions for image preprocessing, blur parameter estimation, and restoration filtering, making it ideal for educational purposes. Accompanying visual examples illustrate the complete restoration pipeline from blurred input to deblurred output. All code is executable and includes test images for practical verification of the algorithm's performance.
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