Open AccessGuided filter‐based multi‐scale super‐resolution reconstruction
Author(s) -
Feng Xiaomei,
Li Jinjiang,
Hua Zhen
Publication year - 2020
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2019.0065
Subject(s) - scale (ratio) , computer science , filter (signal processing) , remote sensing , environmental science , computer vision , artificial intelligence , geology , geography , cartography
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.