Image Quality Enhancement for Single-Image Super Resolution Based on Local Similarities and Support Vector Regression

2011 ◽  
Vol E94-A (2) ◽  
pp. 552-554
Author(s):  
Atsushi YAGUCHI ◽  
Tadaaki HOSAKA ◽  
Takayuki HAMAMOTO
Keyword(s):  
Image Quality ◽  
Support Vector Regression ◽  
Super Resolution ◽  
Support Vector ◽  
Quality Enhancement ◽  
Single Image ◽  
Image Quality Enhancement ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Single image super-resolution reconstruction using support vector regression

2016 ◽  
Vol 24 (9) ◽  
pp. 2302-2309 ◽  
Author(s):  
袁其平 YUAN Qi-ping ◽  
林海杰 LIN Hai-jie ◽  
陈志宏 CHEN Zhi-hong ◽  
杨晓苹 YANG Xiao-ping
Keyword(s):  
Support Vector Regression ◽  
Super Resolution ◽  
Support Vector ◽  
Single Image ◽  
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.

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