Super Resolution of MR Brain Images Using Compressive Sensing and Fuzzy Logical Rules

HIGHLIGHTS

In medical surround, high resolution images are needed to discern the details of image.

Super resolution image can be made from one/few images by means of compressive sensing.

A reckoning soft computing way for resolution enhancement, fuzzy logic is implemented.

Better results are obtained for a dataset with different performance metrics.

Abstract

The proficiency of image processing is of extreme importance in perceiving and collecting information from the images, which includes the process of changing or interpreting existing images. In medical image processing, imaging with more accuracy plays a crucial role in better diagnosis or for the posterior analysis of treatment. Magnetic Resonance Imaging (MRI) is a medicinal creative tool for studying the internal structures and functionalities of human brain, knee, heart, liver, etc. Typical MR scans are essential now for better diagnosis but, limited resolution that is often inadequate for extracting detailed and reliable information. So, for the super resolution (SR) of MR brain images concepts of compressive sensing (CS) & fuzzy logical rules to improve data quality are proposed in this paper. Usually, reconstruction of an SR image is the formation of high resolution (HR) image which is obtained from one or few low resolution (LR) images. In the proposed method, with the help of compressive sensing a very limited number of images are considered even though it’s a challenging task and fuzzy logical rules for a specific membership function are applied to improve the resolution of the image. To assess the performance of the proposal, different metrics are evaluated and achieved better results.

Keywords:
low resolution; super resolution; compressive sensing; fuzzy logical rules