Enhancement of accuracy and specificity in image using novel deblurring method with support vector machine (SVM) compared by Richardson-Lucy deconvolution algorithm

Aim-Image deconvolution is formulated in the field of digital image processing to restore original image using machine learning techniques for impressive results. The goal of this research is to compare execution of Novel SVM algorithm in image deblurring to that of RLD algorithm. Materials and Methods – To deblur the input image up to256 pixel range, Novel Support Vector Machine (SVM). MATLAB software was used to implement these algorithms in order to improve the accuracy rate and specificity of deblurred images. Sample size was determined by using clincalc.com and previous literature, and it was examined by collecting a dataset of 20 samples for each algorithm with a pretest power of 80%. Results – The Novel SVM attains 88.32% accuracy and also 84.33% specificity in image deblurring through MATLAB simulation result. In the same way RLD technique also attains an image deblurring rate of 84.43% accuracy and 80.51% specificity. The value of significance achieved as 0.002, (P