%0 Journal Article %1 noauthororeditor %A Kim, Hyo-Song %A Shin, Jeyong %A Park, Rae-Hong %D 2015 %J Advanced Computational Intelligence: An International Journal (ACII) %K Coding Compensation Estimation Example-Based Image Learning Motion Neigborhood Registration Regression Sparse Super-Resolution %N 2 %P 9 %T JOINT IMAGE REGISTRATION AND EXAMPLE-BASED SUPER-RESOLUTION ALGORITHM %U http://airccse.org/journal/acii/papers/2215acii05.pdf %V 2 %X Supper-resolution (SR) methods are classified into two different methods: image registration (IR)-based methods and example-based methods. The proposed joint SR method is focused on estimating highresolution (HR) video sequences from low-resolution (LR) ones by combining the two different methods. The IR-based SR method collects information from adjacent frames to reconstruct HR images in the video sequence. Example-based SR methods give good textures and strong edges in the result HR video. In this paper, IR-based and example-based SR methods are fused based on the gradient features. The proposed joint SR method gives smaller peak signal to noise ratio than the example-based method, however it shows better reconstruction results on high-level features such as characters in images. Experimental result of the proposed joint SR method shows less noise and higher contrast than the example-based method.

@article{noauthororeditor, abstract = {Supper-resolution (SR) methods are classified into two different methods: image registration (IR)-based methods and example-based methods. The proposed joint SR method is focused on estimating highresolution (HR) video sequences from low-resolution (LR) ones by combining the two different methods. The IR-based SR method collects information from adjacent frames to reconstruct HR images in the video sequence. Example-based SR methods give good textures and strong edges in the result HR video. In this paper, IR-based and example-based SR methods are fused based on the gradient features. The proposed joint SR method gives smaller peak signal to noise ratio than the example-based method, however it shows better reconstruction results on high-level features such as characters in images. Experimental result of the proposed joint SR method shows less noise and higher contrast than the example-based method.}, added-at = {2017-12-20T04:32:04.000+0100}, author = {Kim, Hyo-Song and Shin, Jeyong and Park, Rae-Hong}, biburl = {https://www.bibsonomy.org/bibtex/27bc71367d2e76d74b4a4242d1e211b99/janakirob}, interhash = {7da9b14bb76bcfa39d2fe7555d9b5462}, intrahash = {7bc71367d2e76d74b4a4242d1e211b99}, issn = {2454 - 3934}, journal = {Advanced Computational Intelligence: An International Journal (ACII)}, keywords = {Coding Compensation Estimation Example-Based Image Learning Motion Neigborhood Registration Regression Sparse Super-Resolution}, language = {English}, month = {April}, number = 2, pages = 9, timestamp = {2017-12-20T04:32:04.000+0100}, title = {JOINT IMAGE REGISTRATION AND EXAMPLE-BASED SUPER-RESOLUTION ALGORITHM}, url = {http://airccse.org/journal/acii/papers/2215acii05.pdf}, volume = 2, year = 2015 }