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Improving The Iterative Back Projection Estimation Through Lorentzian Sharp Infinite Symmetrical Filter
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Keywords
Iterative Back Projection
Super-Resolution
Lorentzian Filter
Anisotropic Diffusion
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Abstract
Iterative Back Projection (IBP) remains a foundational technique in single-image and multi-frame super-resolution reconstruction; however, its sensitivity to noise, edge blurring, and reconstruction instability continues to limit performance in high-frequency detail recovery. This study proposes an enhanced IBP estimation framework incorporating a Lorentzian Sharp Infinite Symmetrical Filter (LSISF) to improve edge fidelity, suppress reconstruction artifacts, and stabilize convergence behavior. The proposed approach integrates robust anisotropic diffusion principles and edge-preserving statistical modeling to refine projection error correction in IBP iterations. Building upon robust statistical filtering concepts, particularly anisotropic diffusion formulations, the method enhances structural consistency while maintaining fine texture details (Black et al., 1997). Comparative theoretical analysis demonstrates that the Lorentzian-based symmetrical filtering mechanism improves sensitivity control over high-frequency components, enabling adaptive correction of reconstruction residuals. The framework is evaluated conceptually against classical IBP and learning-based super-resolution models, highlighting improvements in noise resilience, convergence stability, and structural preservation. The findings indicate that integrating robust diffusion-driven filtering into IBP significantly enhances reconstruction fidelity for ill-posed super-resolution problems, particularly in low signal-to-noise environments.
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