scholarly journals Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution

Micromachines ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1418
Author(s):  
Yue Yu ◽  
Kun She ◽  
Jinhua Liu
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Super Resolution ◽  
Frequency Separation ◽  
Wavelet Coefficients ◽  
Low Resolution ◽  
High Quality ◽  
Attention Network ◽  
Resolution Image ◽  
Image Super Resolution

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.

Detection and Tracking of Vehicle Target Based on Super-Resolution Reconstruction and Variable Template Matching

2014 ◽  
Vol 541-542 ◽  
pp. 1429-1432
Author(s):  
Jun Yong Ma ◽  
Shao Dong Chen ◽  
Sheng Wei Zhang
Keyword(s):  
Template Matching ◽  
Super Resolution ◽  
Image Sequence ◽  
Low Resolution ◽  
Matching Method ◽  
Matching Accuracy ◽  
High Quality ◽  
Resolution Image ◽  
Detection And Tracking ◽  
Image Super Resolution

Vehicle Target Detection and Tracking Method Based on Image Super-Resolution Reconstruction and Variable Template Matching is Put Forward. Firstly, a Nonlinear Iterative Algorithm is Applied to Reconstruct a Super-Resolution Image from Low Resolution Image Sequence; then, the Image is Standardized and the Movement Areas are Determined; Finally, the Variable Template Matching Method is Used to Detect and Track the Vehicle Targets in Movement Areas. from the Characteristics of Algorithm and the Experiment Results, we can see that the Proposed Algorithm Improves the Matching Accuracy of Target Tracking and Better Solves the Limitation of Missed Detection for Traditional Methods. the Reason of the Good Performance of the Proposed Algorithm Relies in High Quality Images Acquired by Super-Resolution Reconstruction from Low Resolution Image Sequence and the Application of Variable Template Matching Method.

Medical-Image Super-Resolution Reconstruction Method Based on Residual Channel Attention Network

2020 ◽  
Vol 57 (2) ◽  
pp. 021014
Author(s):  
刘可文 Liu Kewen ◽  
马圆 Ma Yuan ◽  
熊红霞 Xiong Hongxia ◽  
严泽军 Yan Zejun ◽  
周志军 Zhou Zhijun ◽  
...  
Keyword(s):  
Medical Image ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Attention Network ◽  
Image Super Resolution

Distillation

2010 ◽  
pp. 244-264
Author(s):  
Dong Seon Cheng ◽  
Marco Cristani ◽  
Vittorio Murino
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Video Data ◽  
Distillation Process ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
Image Super Resolution ◽  
Comparative Results ◽  
Low Resolution Images

Image super-resolution is one of the most appealing applications of image processing, capable of retrieving a high resolution image by fusing several registered low resolution images depicting an object of interest. However, employing super-resolution in video data is challenging: a video sequence generally contains a lot of scattered information regarding several objects of interest in cluttered scenes. Especially with hand-held cameras, the overall quality may be poor due to low resolution or unsteadiness. The objective of this chapter is to demonstrate why standard image super-resolution fails in video data, which are the problems that arise, and how we can overcome these problems. In our first contribution, we propose a novel Bayesian framework for super-resolution of persistent objects of interest in video sequences. We call this process Distillation. In the traditional formulation of the image super-resolution problem, the observed target is (1) always the same, (2) acquired using a camera making small movements, and (3) found in a number of low resolution images sufficient to recover high-frequency information. These assumptions are usually unsatisfied in real world video acquisitions and often beyond the control of the video operator. With Distillation, we aim to extend and to generalize the image super-resolution task, embedding it in a structured framework that accurately distills all the informative bits of an object of interest. In practice, the Distillation process: i) individuates, in a semi supervised way, a set of objects of interest, clustering the related video frames and registering them with respect to global rigid transformations; ii) for each one, produces a high resolution image, by weighting each pixel according to the information retrieved about the object of interest. As a second contribution, we extend the Distillation process to deal with objects of interest whose transformations in the appearance are not (only) rigid. Such process, built on top of the Distillation, is hierarchical, in the sense that a process of clustering is applied recursively, beginning with the analysis of whole frames, and selectively focusing on smaller sub-regions whose isolated motion can be reasonably assumed as rigid. The ultimate product of the overall process is a strip of images that describe at high resolution the dynamics of the video, switching between alternative local descriptions in response to visual changes. Our approach is first tested on synthetic data, obtaining encouraging comparative results with respect to known super-resolution techniques, and a good robustness against noise. Second, real data coming from different videos are considered, trying to solve the major details of the objects in motion.

Gaussian Noised Single-Image Super Resolution Reconstruction

2013 ◽  
Vol 457-458 ◽  
pp. 1032-1036
Author(s):  
Feng Qing Qin ◽  
Li Hong Zhu ◽  
Li Lan Cao ◽  
Wa Nan Yang
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Projection Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Filtering Algorithm ◽  
Resolution Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.

scholarly journals A Review on Medical Image Super Resolution with Application of Deep Learning

2021 ◽  
Vol 7 (2) ◽  
pp. 25-29
Author(s):  
Kajol Singh ◽  
Manish Saxena
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Loss Functions ◽  
Acquisition Time ◽  
Current State ◽  
Image Acquisition Time ◽  
Image Super Resolution

Super resolution problems are often discussed in medical imaging. The spatial resolution of medical images is insufficient due to limitations such as image acquisition time, low radiation dose or hardware limitations. Various super-resolution methods have been proposed to solve these problems, such as optimization or learning-based approaches. Recently, deep learning methodologies have become a thriving technology and are evolving at an exponential rate. We believe we need to write a review to illustrate the current state of deep learning in super-resolution medical imaging. In this article, we provide an overview of image resolution and the deep learning introduced in super resolution. This document describes super resolution for single images versus super resolution for multiple images, evaluation metrics and loss functions.

Image Super-Resolution via Low-Rank Representation

2014 ◽  
Vol 568-570 ◽  
pp. 652-655 ◽  
Author(s):  
Zhao Li ◽  
Le Wang ◽  
Tao Yu ◽  
Bing Liang Hu
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Low Rank ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
High Resolution Images ◽  
Low Rank Representation ◽  
Image Super Resolution ◽  
Single Image Super Resolution

This paper presents a novel method for solving single-image super-resolution problems, based upon low-rank representation (LRR). Given a set of a low-resolution image patches, LRR seeks the lowest-rank representation among all the candidates that represent all patches as the linear combination of the patches in a low-resolution dictionary. By jointly training two dictionaries for the low-resolution and high-resolution images, we can enforce the similarity of LLRs between the low-resolution and high-resolution image pair with respect to their own dictionaries. Therefore, the LRR of a low-resolution image can be applied with the high-resolution dictionary to generate a high-resolution image. Unlike the well-known sparse representation, which computes the sparsest representation of each image patch individually, LRR aims at finding the lowest-rank representation of a collection of patches jointly. LRR better captures the global structure of image. Experiments show that our method gives good results both visually and quantitatively.

Single-Image Super Resolution Reconstruction with Pepper and Salt Noise

2013 ◽  
Vol 427-429 ◽  
pp. 1817-1821
Author(s):  
Feng Qing Qin ◽  
Li Hong Zhu ◽  
Li Lan Cao ◽  
Wa Nan Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Projection Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Resolution Image ◽  
Imaging Model ◽  
Resolution Imaging ◽  
Image Super Resolution ◽  
Single Image Super Resolution

In order to improve the resolution of single image with Pepper and Salt noise, a framework is proposed. In the low resolution imaging model, the Gaussian blur, down-sampling, as well as Pepper and Salt noise are considered. For the low resolution image, the Pepper and Salt noise is reduced through median filtering method. Super resolution reconstruction is performed on the de-noised low resolution image by iterative back projection algorithm. Experimental results show that the Pepper and Salt noise are removed effectively and the peak signal to noise ratio (PSNR) of the super resolution reconstructed image is improved.

scholarly journals Dynamic Convolutional Neural Network For Image Super-resolution

2018 ◽  
Vol 3 ◽  
pp. 1
Author(s):  
Anil Bhujel ◽  
Dibakar Raj Pant
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Low Resolution ◽  
Scaling Factors ◽  
Resolution Image ◽  
High Resolution Image ◽  
Image Super Resolution ◽  
Up Scaling

<p>Single image super-resolution (SISR) is a technique that reconstructs high resolution image from single low resolution image. Dynamic Convolutional Neural Network (DCNN) is used here for the reconstruction of high resolution image from single low resolution image. It takes low resolution image as input and produce high resolution image as output for dynamic up-scaling factor 2, 3, and 4. The dynamic convolutional neural network directly learns an end-to-end mapping between low resolution and high resolution images. The CNN trained simultaneously with images up-scaled by factors 2, 3, and 4 to make it dynamic. The system is then tested for the input images with up-scaling factors 2, 3 and 4. The dynamically trained CNN performs well for all three up-scaling factors. The performance of network is measured by PSNR, WPSNR, SSIM, MSSSIM, and also by perceptual.</p><p><strong>Journal of Advanced College of Engineering and Management,</strong> Vol. 3, 2017, Page: 1-10</p>

scholarly journals Multi-scale Xception based depthwise separable convolution for single image super-resolution

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0249278
Author(s):  
Wazir Muhammad ◽  
Supavadee Aramvith ◽  
Takao Onoye
Keyword(s):  
Research Work ◽  
Super Resolution ◽  
Single Image ◽  
Low Resolution ◽  
Significant Drop ◽  
Great Ability ◽  
Resolution Image ◽  
Significant Performance ◽  
Image Super Resolution ◽  
Single Image Super Resolution

The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performance in image super-resolution tasks. However, existing approaches related with image super-resolution fail to use the features information of low-resolution images as well as do not recover the hierarchical features for the final reconstruction purpose. In this research work, we have proposed a new architecture inspired by ResNet and Xception networks, which enable a significant drop in the number of network parameters and improve the processing speed to obtain the SR results. We are compared our proposed algorithm with existing state-of-the-art algorithms and confirmed the great ability to construct HR images with fine, rich, and sharp texture details as well as edges. The experimental results validate that our proposed approach has robust performance compared to other popular techniques related to accuracy, speed, and visual quality.

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