Paper Info
Title
Generating High-Quality Air-Coupled Ultrasonic Images For Wooden Material Characterization By Single Image Super-Resolution
Abstract
As the practical applications in other fields, high-resolution images are usually expected to provide a more accurate assessment for the air-coupled ultrasonic (ACU) characterization of wooden materials. This paper investigated the feasibility of applying single image superresolution (SISR) methods to recover high-quality ACU images from the raw observations that were constructed directly by the on-the-shelf ACU scanners. Four state-of-the-art SISR methods were applied to the low-resolution ACU images of wood products. The reconstructed images were evaluated by visual assessment and objective image quality metrics, including peak signal-to-noise-ratio and structural similarity. Both qualitative and quantitative evaluations indicated that the substantial improvement of image quality can be yielded. The results of the experiments demonstrated the superior performance and high reproducibility of the method for generating high-quality ACU images. Sparse coding based super-resolution and super-resolution convolutional neural network (SRCNN) significantly outperformed other algorithms. SRCNN has the potential to act as an effective tool to generate higher resolution ACU images due to its flexibility.
Year
DOI
Venue
2020
10.1142/S0218001420540087
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Air-coupled ultrasonic imaging, wooden material characterization, super-resolution, convolutional neural networks
Journal
34
Issue
ISSN
Citations 
3
0218-0014
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Lujun Lin100.34
Yiming Fang2309.71
Xiaochen Du301.01
Zhu Zhou400.34