Notice of Removal: Automated super-resolution image processing in ultrasound using machine learning

2017 ◽  
Author(s):  
Kirsten Christensen Jeffries ◽  
Markus Schirmer ◽  
Jemma Brown ◽  
Sevan Harput ◽  
Meng-Xing Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Super Resolution ◽  
Resolution Image

Super-Resolution Analysis on Molten Pool Image of Metal Active-Gas Welding Based on Wavelet with Fractal

2011 ◽  
Vol 103 ◽  
pp. 152-157
Author(s):  
Guang Zhi Dai ◽  
Guo Qiang Han ◽  
Chao Yi Dong
Keyword(s):  
Image Processing ◽  
Super Resolution ◽  
Image Interpolation ◽  
Image Resolution ◽  
Mag Welding ◽  
Resolution Image ◽  
Fractal Image ◽  
Welding Image ◽  
Resolution Analysis ◽  
Wavelet Image

According to the unique advantages in image processing combining wavelet and fractal and the different ways of combination, a super-resolution image processing methods are proposed. The methods are characterized by combining the wavelet transform, Wavelet Image Interpolation and FBM Fractal Image interpolation in a certain way to achieve super-resolution image reconstruction. Through processing MAG welding pool images polluted by noises seriously, the results show that: the method proposed in this paper, compared with the method based on wavelet bilinear interpolation, not only effectively raises MAG welding image resolution, but also PSNR of reconstruction images are enhanced 21.1049 dB.

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

Research of Super-Resolution Image Reconstruction Methods

2014 ◽  
Vol 687-691 ◽  
pp. 3782-3786
Author(s):  
Ling Tang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Reconstruction ◽  
Development Process ◽  
Super Resolution ◽  
Resolution Image ◽  
Reconstruction Methods ◽  
Research Findings

The super-resolution image reconstruction has become a hot topic in the areas of image processing and computer vision because of its extensive theoretical and practical values. This paper described the concept of super-resolution reconstruction, reviewed the development process of the technique, common algorithms classification, the current research findings and other related issues. The characteristics of different algorithms are also analyzed.

Inverse halftoning using super-resolution image processing

2011 ◽  
Vol 7 (2) ◽  
pp. 208-213 ◽  
Author(s):  
Yuki Minami ◽  
Shun-ichi Azuma ◽  
Toshiharu Sugie
Keyword(s):  
Image Processing ◽  
Super Resolution ◽  
Resolution Image ◽  
Inverse Halftoning
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