Medical-Modality Super-resolution for increased visualisation of Intracranial Tissue Details and Structural Details

Numerous efforts have been made to produce high-resolution images for medical imaging equipment. The procedure for image acquisition in the medical imaging field however not invariably produce high quality images that can be beneficial for clinical diagnosis. Super resolution (SR) imaging has become a common area of research in medical imaging in particular nowadays. Because of its wide practical applications, image SR has drawn great interest in the domain of image processing community. The purpose of image SR is to produce high quality images from the low-quality counterparts. The field of imaging has seen significant improvements in resolution and image quality over the past few decades, with the aid of improved effective super resolution algorithms. The study proposes an efficient SR method based on wiener filtering via adaptive line search method. The proposed method initially employs a wiener filtering which recovers the feature of the images by inverse filtering of low-quality source images. Further, an adaptive line search method is utilized for fast convergence, in which an approximate analytical term of step size is proposed in order to prevent us from setting it empirically. In addition, a proposed line search method further modifies in order to be more adaptive under various SR circumstances. In the end, the method uses a recursive filtering in transform domain which adequately helps to retains the edges of the source images. An experimental evaluation is performed on numerous medical image data sets. In terms of both quantitative metrics and visual analysis, the proposed strategy exhibits higher performance as compares to prevailing state-of-the-art methods.