A new approach of image denoising based on adaptive multi-resolution technique

Abstract

Medical imaging and diagnostic techniques have become popular over the last two decades with the advancement of data science, data analysis, data storage, and the internet. The impact of this evolution can be seen in the fields of telemedicine and medical sciences, which allow more effective detection and treatment of various diseases. Like any other form of imaging technique, medical images are sensitive to noise and artifacts. The images become unclear with the presence of noise, and the diseases cannot be identified properly. Therefore, image denoising plays a vital role in the field of biomedical image processing. As a result, work must be done to minimize noise without sacrificing image quality. Various methods for reducing noise have already been proposed in the literature. Each method has its own set of benefits and drawbacks. In this paper, we introduce a bi-dimensional empirical mode decomposition (BEMD)-based image de-noising approach. The principal purpose of this research is to decompose noisy images depending on frequency and create a hybrid algorithm that incorporates existing de-noising approaches. The proposed algorithm is an image-dependent technique that decomposes the noisy image into several IMFs with residue, then considering the individual attributes of the IMFs, they are separately filtered. Furthermore equalization is applied to the residue for preserving the edge information. A comprehensive study is conducted over the experimental results of the benchmark test images using different performance measure matrices to quantify the effectiveness of the presented approach. In terms of subjective and objective evaluation, the reconstructed image is found to be more accurate and visually pleasing. It also outperforms the state-of-the-art image-denoising methods, especially in terms of PSNR, RMSE, correlation, and structural similarity.