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 Fractal Image Interpolation: A Tutorial and New Result

2019 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Chi Kok ◽  
Wing Tam
Keyword(s):  
Image Interpolation ◽  
Spatial Domain ◽  
Fractal Interpolation ◽  
Image System ◽  
Pixel Intensity ◽  
Resolution Image ◽  
Fractal Image ◽  
Pros And Cons ◽  
Simulation Results ◽  
Visual Artifacts

This paper reviews the implementation of fractal based image interpolation, the associated visual artifacts of the interpolated images, and various techniques, including novel contributions, that alleviate these awkward visual artifacts to achieve visually pleasant interpolated image. The fractal interpolation methods considered in this paper are based on the plain Iterative Function System (IFS) in spatial domain without additional transformation, where we believe that the benefits of additional transformation can be added onto the presented study without complication. Simulation results are presented to demonstrate the discussed techniques, together with the pros and cons of each techniques. Finally, a novel spatial domain interleave layer has been proposed to add to the IFS image system for improving the performance of the system from image zooming to interpolation with the preservation of the pixel intensity from the original low resolution image.

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 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.

New FCM Segmentation Approach Based on Multi-Resolution Analysis

2018 ◽  
Vol 7 (4) ◽  
pp. 100-114
Author(s):  
Yaghmorasan Benzian ◽  
Nacéra Benamrane
Keyword(s):  
Fuzzy Clustering ◽  
Image Resolution ◽  
Pixel Classification ◽  
Resolution Image ◽  
Fuzzy C Means ◽  
Multi Resolution Analysis ◽  
Segmentation Approach ◽  
Resolution Analysis ◽  
Homogeneous Regions ◽  
Parameter Values

This article presents a modified Fuzzy C Means segmentation approach based on multi-resolution image analysis. Fuzzy C-Means standard methods are improved through fuzzy clustering at different image resolution levels by propagating fuzzy membership values pyramidally from a lower to a higher level. Processing at a lower resolution image level provides a rough pixel classification result, thus, a pixel is assigned to a cluster to which the majority of its neighborhood pixels belongs. The aim of fuzzy clustering with multi-resolution images is to avoid pixel misclassification according to the spatial cluster of the neighbourhood of each pixel in order to have more homogeneous regions and eliminate noisy regions present in the image. This method is tested particularly on samples and medical images with gaussian noise by varying multiresolution parameter values for better analysis. The results obtained after multi-resolution clustering are giving satisfactory results by comparing this approach with standard FCM and spatial FCM ones.

scholarly journals Low Resolution Face Recognition Using Generative Adversarial Network (GAN)

2020 ◽  
Author(s):  
Howard Martin ◽  
Suharjito
Keyword(s):  
Face Recognition ◽  
Super Resolution ◽  
Image Data ◽  
Face Image ◽  
Image Resolution ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
Surveillance Camera ◽  
Adversarial Network

Abstract Face recognition has a lot of use on smartphone authentication, finding people, etc. Nowadays, face recognition with a constrained environment has achieved very good performance on accuracy. However, the accuracy of existing face recognition methods will gradually decrease when using a dataset with an unconstrained environment. Face image with an unconstrained environment is usually taken from a surveillance camera. In general, surveillance cameras will be placed on the corner of a room or even on the street. So, the image resolution will be low. Low-resolution image will cause the face very hard to be recognized and the accuracy will eventually decrease. That is the main reason why increasing the accuracy of the Low-Resolution Face Recognition (LRFR) problem is still challenging. This research aimed to solve the Low-Resolution Face Recognition (LRFR) problem. The datasets are YouTube Faces Database (YTF) and Labelled Faces in The Wild (LFW). In this research, face image resolution would be decreased using bicubic linear and became the low-resolution image data. Then super resolution methods as the preprocessing step would increase the image resolution. Super resolution methods used in this research are Super resolution GAN (SRGAN) [1] and Enhanced Super resolution GAN (ESRGAN) [2]. These methods would be compared to reach a better accuracy on solving LRFR problem. After increased the image resolution, the image would be recognized using FaceNet. This research concluded that using super resolution as the preprocessing step for LRFR problem has achieved a higher accuracy compared to [3]. The highest accuracy achieved by using ESRGAN as the preprocessing and FaceNet for face recognition with accuracy of 98.96 % and Validation rate 96.757 %.

Super-resolution Image Reconstruction from Projections Using Non-local and Local Regularizations

2020 ◽  
Author(s):  
Xue Ren ◽  
Soo-Jin Lee
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Weighting Factor ◽  
Image Resolution ◽  
Resolution Image ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Non Local ◽  
Quality Assessments ◽  
Sr Method

This article presents a super-resolution (SR) method dedicated to tomographic imaging, where an image is reconstructed from projections obtained with low-resolution detectors. In this work, upscaling the image resolution is performed by backprojecting the projection measurements into the high-resolution image space modeled on a finer grid. Since this upscaling process often creates irregular pixels, it is important to employ regularizers that can reduce the irregular pixels while preserving fine details. Here we consider two different types of regularizers, non-local and local regularizers, each of which has been independently used for image reconstruction and is known to have its own advantages and disadvantages depending on the edge structures in the underlying image. To achieve a good compromise between the two types of regularizers, we selectively combine them using a space-variant weighting factor, which is systematically determined by our own criterion to classify edges. The experimental results show that our proposed SR method improves the reconstruction accuracy in various image quality assessments and has the potential to be useful in a wide range of imaging applications.

Sign in / Sign up

Export Citation Format

Share Document