Algorithm for Blurred Image Restoration Using Regularization Methods
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This document presents an algorithm for restoring blurred images utilizing regularization methods. The algorithm processes degraded images to produce enhanced, clearer outputs. For comprehensive evaluation, we introduce artificial noise into the generated images and perform restoration by optimizing regularization parameters. Key implementation aspects include: employing Tikhonov regularization to handle ill-posed problems, using gradient descent or conjugate gradient methods for optimization, and incorporating noise models (Gaussian/salt-and-pepper) for realistic simulation. The algorithm effectively improves image quality by reducing artifacts and enhancing edge preservation through proper parameter tuning, making blurred content more distinguishable and visually interpretable.
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