An efficient grey wolf optimization algorithm based extended kalman filtering technique for various image modalities restoration process

• Published in: Multimedia tools and applications 2018-12, Vol.77 (23), p.30205-30232
• Main Authors: Baron Sam, B., Lenin Fred, A.
• Format: Article
• Language: English
• Subjects: Algorithms; Computed tomography; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Extended Kalman filter; Filtration; Image contrast; Image degradation; Image quality; Image reconstruction; Image restoration; Kalman filters; Liver; Magnetic resonance imaging; Medical imaging; Multimedia Information Systems; Optimization; Optimization algorithms; Procurement; Signal to noise ratio; Soft computing; Special Purpose and Application-Based Systems
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-018-6088-0

The procedure of procurement the original image from the degraded image assumed the knowledge of the debasing factors is called as Image Restoration. The corrupted image will be considered as input and provided to the soft computing method for decreasing the noisy information from the input image in our suggested method. Nonetheless, the established output image obtained from the soft computing method will seem to be blurry with less superiority in the contrast level. This blurred information damages the function of image reconstruction. Thus, into overawed this disadvantage, we deed an Extended Kalman filter method to provide great quality reconstructed the image, in that an optimization algorithm Grey Wolf Optimization (GWO) will be used for a greater reconstructed image. The main contribution of the proposed work is to improve the image quality of reconstruction image by means of optimal Extended Kalman filter. For proving the function of our suggested method, the reconstructed image quality will be associated with the conventional methods. The restoration method was tested with different image modalities such as MRI, CT and also Ultra Sound images of the Human abdomen. The method will be applied to the functioning platform of MATLAB. The implemented restoration technique achieves the maximum Peak Signal to Noise Ratio (PSNR) and structural similarity index (SSIM) value for liver CT image are 35.10db and 0.999 and minimum Mean square error (MSE) and Mean absolute error (MAE) value for liver CT image are 7.90E-05 and 1.1281 respectively.