Title :
Single Image Super-Resolution Based on Support Vector Regression
Author :
Li, Dalong ; Simske, Steven ; Mersereau, Russell M.
Author_Institution :
Hewlett-Packard Lab., Fort Collins
Abstract :
Motivated by the success of support vector regression (SVR) in blind image deconvolution, we apply SVR to single-frame super-resolution. Initial results show that even when trained on as little as a single image, SVR is able to learn a generally applicable model that can super-resolve dissimilar images.
Keywords :
image resolution; learning (artificial intelligence); regression analysis; support vector machines; machine learning; single image super-resolution; single-frame super-resolution; support vector regression; Deconvolution; Discrete cosine transforms; Discrete wavelet transforms; Filtering; High-resolution imaging; Image resolution; Interpolation; Low pass filters; PSNR; Signal resolution;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371420
