Enhancing Image Quality via Style Transfer for Single Image Super-Resolution

2018 ◽  
Vol 25 (4) ◽  
pp. 571-575 ◽  
Author(s):  
Xin Deng
Keyword(s):  
Image Quality ◽  
Super Resolution ◽  
Single Image ◽  
Style Transfer ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Generating High-Quality Air-Coupled Ultrasonic Images for Wooden Material Characterization by Single Image Super-Resolution

2019 ◽  
Vol 34 (03) ◽  
pp. 2054008
Author(s):  
Lujun Lin ◽  
Yiming Fang ◽  
Xiaochen Du ◽  
Zhu Zhou
Keyword(s):  
Image Quality ◽  
Super Resolution ◽  
Visual Assessment ◽  
Wood Products ◽  
Superior Performance ◽  
Single Image ◽  
High Quality ◽  
Practical Applications ◽  
Image Super Resolution ◽  
Single Image Super Resolution

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 super-resolution (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.

scholarly journals Single image super-resolution via Image Quality Assessment-Guided Deep Learning Network

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0241313
Author(s):  
Zhengqiang Xiong ◽  
Manhui Lin ◽  
Zhen Lin ◽  
Tao Sun ◽  
Guangyi Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Super Resolution ◽  
Single Image ◽  
Learning Network ◽  
Image Super Resolution ◽  
Deep Learning Network ◽  
Single Image Super Resolution
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