High dimensional Bio medical images denoising using wavelet transform and modified bilateral filter
Muhammad Usama Jabbar, Ali Waqar, Muhammad Awais, Zohaib Mushtaq, Muhammad Jamshed Abbass, Muhammad Abdul Rehman, Faisal Saleem, Shehzad Ahmed, Ahmed Zubair Jan · 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) · 2022
Biomedical images consist of salient information that helps in diagnosis of diseases. Significant amount of information is lost during process of transmission due to addition of noisy components especially for Computed Tomography (CT) scan, Ultrasound imaging and Magnetic Resonance Imaging (MRI). Image denoising is an important and necessary pre-processing phase in image applications. Numerous techniques have been developed for denoising process over decades. The purpose is to remove noise from images. Wavelet transform is current state of the art addition to analyze image denoising; able to achieve improved experimental results than from existing techniques. Generalized Gaussian distribution is considered to model noise. In this article, we introduce mix adaptive thresholding with modified bilateral filter. Simulation results demonstrate that attributes of images are improved. The Qualitative and quantitative assessment proves better denoised results for proposed method using parameters like Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS) and Structural Similarity Index (SSIM) between the true and estimated biomedical images.