Detection and Classification of Blur Images using Multi-Class Support Vector Machine

In recent technology, it has been critical for blind image restoration. It is focused on the blur classification of digital images using a Multi-class Support Vector Machine (MSVM) structure. This work aims to classify the blur images using MSVM. MSVM classifier is designed to identify three types of images like Sharp, Defocused, and Motion blurred images. Several experiments are conducted for a sample data called Beihang Univ. Blur Image Database (BHBID). The Mean, Variance, and maximum edge detected feature matrix are taken for each image applied on Sobel, Laplacian, and Roberts cross edge detections. Based on sampling, features are selected to train each member of the MSVM classifier. Using different kernels of SVM's like Linear, Polynomial, Radial Basis Function (RBF), Gaussian, it can optimize the parameters, and the performance metrics like accuracy will be compared. Finally, our proposed system achieved 95.7 % accuracy in finding the defined scenarios