Digital Image Processing: Image Restoration Algorithms Source Code

In this article, we provide source code implementations for image restoration algorithms in digital image processing. These algorithms include Wiener Filtering, Least Squares Method, and Lucy-Richardson (L-R) Algorithm. These techniques are primarily used to restore images degraded by noise, blur, or other distortion factors. To facilitate better understanding of the algorithms' working principles, we detail their implementation steps and underlying mathematical foundations.

For the Wiener Filter implementation, we employ frequency domain processing using Fast Fourier Transform (FFT) operations, incorporating noise-to-signal power ratio estimation for optimal restoration. The Least Squares approach applies regularization techniques to solve the inverse problem of image deblurring, typically implemented through matrix operations and optimization solvers. The Lucy-Richardson algorithm utilizes an iterative deconvolution method that applies maximum likelihood estimation, progressively refining the restored image through multiple iterations.

We also analyze the comparative advantages and limitations of each algorithm, discussing their practical constraints and suitability for different application scenarios. Through studying this material, readers will gain fundamental knowledge of these image restoration algorithms and be able to apply them effectively in real-world digital image processing projects. Each implementation includes error handling mechanisms and parameter optimization guidelines to ensure robust performance.