Open AccessJoint artifact correction and super-resolution of image slicing and mapping system via a convolutional neural network
Author(s) -
An-Qi Liu,
Xianzi Zeng,
Yan Yuan,
Lijuan Su,
Wanyue Wang
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.413076
Subject(s) - computer science , artificial intelligence , artifact (error) , computer vision , convolutional neural network , image resolution , joint (building) , image processing , slicing , image (mathematics) , pattern recognition (psychology) , computer graphics (images) , architectural engineering , engineering
As the key component of the image mapping spectrometer, the image mapper introduces complex image degradation in the reconstructed images, including low spatial resolution and intensity artifacts. In this paper, we propose a novel image processing method based on the convolutional neural network to perform artifact correction and super-resolution (SR) simultaneously. The proposed joint network contains two branches to handle the artifact correction task and SR task in parallel. The artifact correction module is designed to remove the artifacts in the image and the SR module is used to improve the spatial resolution. An attention fusion module is constructed to combine the features extracted by the artifact correction and SR modules. The fused features are used to reconstruct an artifact-free high-resolution image. We present extensive simulation results to demonstrate that the proposed joint method outperforms state-of-the-art methods and can be generalized to other image mapper designs. We also provide experimental results to prove the efficiency of the joint network.