Deblurring ICF Images Using Hybrid Iterative Regularization Techniques
Tuwanda McKenzie, Naima Naheed · 2023
Image deblurring poses a formidable challenge within image processing, as it seeks to restore crisp and aesthetically pleasing visuals from blurry or degraded counterparts. This research is focused on images captured from Inertial Confinement Fusion (ICF) with high-speed X-ray cameras. It is observed that no model fits the noise characteristics of ICF images. So, we initiated our work with a synthetic image in which we simulated blur and noise. Our primary objective is to reconstruct the image from the distorted synthetic version. To accomplish this, we utilized four distinct Iterative Hybrid Regularization methods. SSIM score allowed us to compare the performance of the restored image against our synthetic image.