چکیده مقاله
Recent studies have shown that the performance of single image super resolution methods can be significantly boosted by using deep convolutional neural networks Lately a method has been proposed which directly learns an end to end mapping between the low/high resolution images This mapping is done by a deep Convolutional Neural Network CNN that takes the lowresolution image as the input and outputs the high resolution one In this paper, we enhance this method by using the canny edge detection method A combination of Deep CNNs and Skip connection layers are used as a feature extractor for image features, Moreover, deconvolution layers are integrated into the network to learn the up sampling filters and to speed up the reconstruction process The results of the proposed method shows that considering the result of Canny edge detection in producing the high resolution image, results in better output
کلیدواژهها
نویسندگان
Melika A’la
Department of Mathematics and Computer Science ,Amirkabir University of Technology;
Mohammad Ebrahim Shiri
Department of Mathematics and Computer Science, Amirkabir University of Technology;
Hedieh Sajedi
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran;
شیوه ارجاع
A’la, Melika and Shiri, Mohammad Ebrahim and Sajedi, Hedieh,1398,Image Super-Resolution using Deep learning,3rd International Conference on Soft Computing,Rudsar
ارائهشده در
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