NDLI: Guided filter-based multi-scale super-resolution reconstruction

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Guided filter-based multi-scale super-resolution reconstruction

Content Provider	IET Digital Library
Author	Feng, Xiaomei Li, Jinjiang Hua, Zhen
Abstract	The learning-based super-resolution reconstruction method inputs a low-resolution image into a network, and learns a non-linear mapping relationship between low-resolution and high-resolution through the network. In this study, the multi-scale super-resolution reconstruction network is used to fuse the effective features of different scale images, and the non-linear mapping between low resolution and high resolution is studied from coarse to fine to realise the end-to-end super-resolution reconstruction task. The loss of some features of the low-resolution image will negatively affect the quality of the reconstructed image. To solve the problem of incomplete image features in low-resolution, this study adopts the multi-scale super-resolution reconstruction method based on guided image filtering. The high-resolution image reconstructed by the multi-scale super-resolution network and the real high-resolution image are merged by the guide image filter to generate a new image, and the newly generated image is used for secondary training of the multi-scale super-resolution reconstruction network. The newly generated image effectively compensates for the details and texture information lost in the low-resolution image, thereby improving the effect of the super-resolution reconstructed image.Compared with the existing super-resolution reconstruction scheme, the accuracy and speed of super-resolution reconstruction are improved.
Starting Page	128
Ending Page	140
Page Count	13
Volume Number	5
e-ISSN	24682322
Issue Number	Issue 2, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/trit/5/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0065
Journal	CAAI Transactions on Intelligence Technology
Publisher	The Institution of Engineering and Technology Chongqing University of Technology Chinese Association for Artificial Intelligence
Publisher Date	2020-04-01
Access Restriction	Open
Rights License	Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Computer Vision And Image Processing Technique End-to-end Super-resolution Reconstruction Task Filtering Method in Signal Processing Guide Image Filter Guided Filter-based Multiscale Super-resolution Reconstruction Guided Image Filtering High-resolution Image Image Filtering Image Reconstruction Image Resolution Image Texture Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Learning-based Super-resolution Reconstruction Method Low-resolution Image Loss Multiscale Super-resolution Network Multiscale Super-resolution Reconstruction Method Multiscale Super-resolution Reconstruction Network Newly Generated Image Numerical Analysis Optical, Image And Video Signal Processing Super-resolution Reconstruction Effect Super-resolution Reconstruction Scheme
Content Type	Text
Resource Type	Article

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