CROSS STYLE IMAGE SYNTHESIS THROUGH SEMI COUPLED DICTIONARY LEARNING AND GENERALIZED CROSS VALIDATION ALGORITHM

In various Computer vision applications for better visualization, interpretation and better recognition, we frequently want to change an image in one style into another one which is a large research area of compressive sensing. Algorithms on compressed sensing are in demand to create better reconstruction of the images while taking less computational time and requiring less storage capacity. In the present work attempts are put in the same direction and brought an algorithm which includes semi coupled dictionary learning (SCDL) model of compressed sensing with Singular Value Decomposition (SVD) based Generalized Cross Validation (GCV) algorithm. Using this algorithm one can find the different value of regularization parameters for different sizes of images and that helps to get better reconstruction of the images with ease while doing experiments on cross style image synthesis problems.