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14 August 2019 Improving resolution images detail features for Generate Network
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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111790L (2019) https://doi.org/10.1117/12.2540281
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
Update of deep network framework to super-resolution reconstruction has been greatly improved, but there are great problems of loss of texture information and decreased image detail quality. In this paper, we have constructed network focus on texture features structure, which can generate SR images by taking full advantage of low resolution images and improve the efficiency of generation. In our method, we first adopt extract detail texture information by kernel diversity network (KDN)which is a combined with residual network to extensive extract various feature of low dimensional images. Particularly, KDN is derived from the processing of the original image and has the ability to prevent information loss and its operation according to certain combination mode by convolution operations with different properties. Furthermore, we design pyramid amplification networks that improving generation speed and image quality to maximizing utilization information of the original image. Our final results show that an SR network with KDN and pyramid networks can generate more natural and clear texture in comparison to state-of-the-art methods.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jing Liu, Jianhui Ge, and Rui Xue "Improving resolution images detail features for Generate Network", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790L (14 August 2019); https://doi.org/10.1117/12.2540281
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