Integrated Gan And Ir Algorithms For Noise-robust Medical Image Reconstruction

Abstract

Medical Imaging Techniques Such As Magnetic Resonance Imaging And Computed Tomography Play A Vital Role In Disease Diagnosis And Treatment Planning. However, Medical Images Are Often Degraded By Noise Caused By Low Radiation Exposure, Patient Motion, Hardware Limitations, And Quantum Interference. Such Degradation Reduces Image Clarity And May Affect Clinical Decision Making. Conventional Denoising Approaches, Including Wavelet Transforms, Non Local Means Filtering, And Total Variation Minimization, Frequently Struggle To Remove Noise While Preserving Delicate Anatomical Structures. This Paper Presents A Hybrid Framework That Integrates Generative Adversarial Networks, Iterative Reconstruction Algorithms, And Advanced Generative Artificial Intelligence For Effective Medical Image Denoising. The Generative Adversarial Component Models Complex Noise Patterns To Produce Preliminary Clean Images, While The Iterative Reconstruction Process Refines Structural Details Through Optimization. In Addition, Semantic Aware Enhancement Improves Contextual Understanding Of Anatomical Features. Experimental Evaluation On Publicly Available Medical Imaging Datasets Demonstrates Superior Performance In Terms Of Peak Signal To Noise Ratio, Structural Similarity Index, And Mean Squared Error. The Proposed Framework Enhances Diagnostic Reliability And Supports Improved Outcomes In Radiology, Oncology, And Telemedicine Applications.

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