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• W3195587117 abstract "Generative Adversarial Network (GAN) based models have gained a lot of popularity due to their outstanding performance in image super resolution tasks. However, these networks have few inherent problems; such as, high computational complexity, large depth and vanishing gradient. Present work proposes a novel GAN based architecture, namely, Super Resolution with Inception Network (SRINet) to solve the above mentioned problems. Generator architecture of SRINet uses complex filter structure rather than the linear filter structure to increase the depth and width of network without increasing the computational cost. Complex filter settings, present in the architecture, helps it to attain locally distributed information along with hierarchical global information in an image. Hence, the proposed method approximates the most favorable sparse structures to foster the learning capability of network. Additionally, progressive two stage upscaling approach with dense skip connections are introduced in the generator architecture. This technique helps the proposed network to learn precise mapping to generate an output image from the low resolution image. To measure visual quality of an image, we use human visual system based visual information fidelity metric. Proposed method outperforms all the state-of-the-art methods qualitatively (perceptually) and quantitatively on other GAN based methods." @default.
• W3195587117 created "2021-08-30" @default.
• W3195587117 creator A5003715265 @default.
• W3195587117 creator A5012738320 @default.
• W3195587117 date "2021-12-01" @default.
• W3195587117 modified "2023-10-06" @default.
• W3195587117 title "An efficient image super resolution model with dense skip connections between complex filter structures in Generative Adversarial Networks" @default.
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• W3195587117 doi "https://doi.org/10.1016/j.eswa.2021.115780" @default.
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