Super-Resolution of Digital Images using CNN with Leaky ReLU
2019 ◽
Vol 8
(2S11)
◽
pp. 210-212
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Keyword(s):
Image super-resolution (SR) has been used in many real world applications as a preprocessing phase. The improvement in image resolution increases the performance of image analysis process. The SR of digital images is achieved by taking the low resolution images as inputs. In this article, a novel deeplearning based super-resolution approach is proposed. The proposed approach uses Convolutional Neural Network (CNN) with leaky rectified linear unit (ReLU) for learning and generalization. The experiments with test images taken from USC-SIPI dataset indicate that the proposed approach increases the quality of the images in terms of the quantitative metric peak signal to noise ratio.
2019 ◽
Vol 8
(6)
◽
pp. 3026-3030
2021 ◽
Keyword(s):
2020 ◽
Vol 18
(06)
◽
pp. 2050049
2012 ◽
Vol 29
(6)
◽
pp. 772-795
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Keyword(s):