Super resolution in image processing

2002 ◽  
Author(s):  
A. Feuer
Keyword(s):  
Image Processing ◽  
Super Resolution

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 Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

2020 ◽  
Vol 7 (3) ◽  
pp. 432
Author(s):  
Windi Astuti
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Super Resolution ◽  
Linear Interpolation ◽  
Resolution Method ◽  
Image Zooming ◽  
Image Enlargement ◽  
Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

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

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.

scholarly journals Instant super-resolution imaging in live cells and embryos via analog image processing

2013 ◽  
Vol 10 (11) ◽  
pp. 1122-1126 ◽  
Author(s):  
Andrew G York ◽  
Panagiotis Chandris ◽  
Damian Dalle Nogare ◽  
Jeffrey Head ◽  
Peter Wawrzusin ◽  
...  
Keyword(s):  
Image Processing ◽  
Super Resolution ◽  
Live Cells ◽  
Resolution Imaging ◽  
Analog Image Processing

scholarly journals Research on Image Denoising and Super-Resolution Reconstruction Technology of Multiscale-Fusion Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Size Li ◽  
Pengjiang Qian ◽  
Xin Zhang ◽  
Aiguo Chen
Keyword(s):  
Image Processing ◽  
Receptive Field ◽  
Image Denoising ◽  
State Of The Art ◽  
Experimental Studies ◽  
Super Resolution ◽  
Image Resolution ◽  
Reconstruction Method ◽  
Model Learning ◽  
Image Super Resolution

Image denoising and image super-resolution reconstruction are two important techniques for image processing. Deep learning is used to solve the problem of image denoising and super-resolution reconstruction in recent years, and it usually has better results than traditional methods. However, image denoising and super-resolution reconstruction are studied separately by state-of-the-art work. To optimally improve the image resolution, it is necessary to investigate how to integrate these two techniques. In this paper, based on Generative Adversarial Network (GAN), we propose a novel image denoising and super-resolution reconstruction method, i.e., multiscale-fusion GAN (MFGAN), to restore the images interfered by noises. Our contributions reflect in the following three aspects: (1) the combination of image denoising and image super-resolution reconstruction simplifies the process of upsampling and downsampling images during the model learning, avoiding repeated input and output images operations, and improves the efficiency of image processing. (2) Motivated by the Inception structure and introducing a multiscale-fusion strategy, our method is capable of using the multiple convolution kernels with different sizes to expand the receptive field in parallel. (3) The ablation experiments verify the effectiveness of each employed loss measurement in our devised loss function. And our experimental studies demonstrate that the proposed model can effectively expand the receptive field and thus reconstruct images with high resolution and accuracy and that the proposed MFGAN method performs better than a few state-of-the-art methods.

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