• Dr.B.Nancharayya
• Mrs.V.Praveena
• P.Manusha
• P.Vanitha
• P.Akhila

The reconstruction of High Resolution (HR) image from single input Low Resolution (LR) image is main criteria in this paper. Here, we utilize an effective way using mixture prior models to transform the large nonlinear feature space of LR images into a group of linear sub spaces in the training phase. In particular, we first partition image patches into several groups by a novel selective patch processing (SPP) method based on difference curvature of LR patches, and then learning the mixture prior models in each group. Moreover, different prior distributions have various effectiveness in super-resolution, in this case, we find that A-Student-t prior shows stronger performance than the well-known Gaussian prior and student-t prior model. In the testing phase, we adopt the learned multiple mixture prior models to map the input LR features into the appropriate subspace, and finally reconstruct the corresponding HR image in a novel mixed matching way. Experimental results indicate that the proposed approach is both quantitatively and qualitatively superior to some state-of-the-art SR methods.

A-Student-t mixture model, Selective patch processing, Difference curvature, Mixed matching.

"Single Image Super Resolution via Advanced Multiple Mixture Prior model.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.360-370, April 2020, Available :http://www.jetir.org/papers/JETIR2004542.pdf

"Single Image Super Resolution via Advanced Multiple Mixture Prior model.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp360-370, April 2020, Available at : http://www.jetir.org/papers/JETIR2004542.pdf