Biomedical Image Restoration with Advanced Noise Reduction Techniques for Enhanced Brain MRI Image Quality.

Abstract

Biomedical imaging, especially in the context of brain images, often suffers from degradation that compromises the quality and interpretability of the images. Such degradation can manifest as blurs from various sources, including out-of-focus conditions, motion artifacts, disturbances, and the addition of Gaussian noise. Restoring these images to their original quality is crucial for accurate diagnosis and analysis in clinical settings. This article presents a comprehensive study on advanced restoration techniques tailored for brain imaging. It addresses multiple types of degradations, including out-of-focus blur, motion blur, atmospheric turbulence blur, and Gaussian noise. The restoration methods discussed encompass classical approaches, regularization parameter estimation, and point spread function (PSF) estimation. The study contributes in five key areas: four of these focus on spatially invariant degradations, while one explores the challenges associated with spatially variant degradations. Two innovative schemes are proposed for estimating motion blur parameters. The first scheme utilizes a two-dimensional median filter to identify the direction of the blur, while the second employs a statistics order filter to determine the length of the blur. Following parameter estimation, a Wiener filter is applied to restore the images, effectively mitigating the identified blurs and noise. The robustness of the proposed techniques is rigorously tested under various noise conditions to ensure their effectiveness. The results demonstrate significant improvements in the quality of brain images, enhancing their utility for diagnostic purposes. This research highlights the potential of these advanced restoration techniques to improve the clarity and precision of brain imaging in biomedical applications.