scholarly journals Super-Resolution Reconstruction Algorithm for Infrared Image with Double Regular Items Based on Sub-Pixel Convolution

2020 ◽  
Vol 10 (3) ◽  
pp. 1109
Author(s):  
Lei Yu ◽  
Xuewei Zhang ◽  
Yan Chu
Keyword(s):  
Infrared Image ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Objective Evaluation ◽  
Evaluation Indexes ◽  
Multi Scale ◽  
Adaptive Parameters ◽  
Dual Regularization ◽  
Convolution Algorithm ◽  
High Resolution Images

In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convolution algorithm are combined to enrich the details; then, a regularization function with two adaptive parameters and two regularization terms is proposed to enhance the edge. MPSR firstly enhances the multi-scale detail of low-resolution images; then, regular processing and feature extraction are carried out; finally, sub-pixel convolution is used to fuse the extracted features to generate high-resolution images. The experimental results show that the subjective and objective evaluation indexes (PSNR/SSIM) of the algorithm have achieved satisfactory results.

Multi-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image

2017 ◽  
Vol 76 (23) ◽  
pp. 24871-24902 ◽  
Author(s):  
Xiaomin Yang ◽  
Wei Wu ◽  
Kai Liu ◽  
Weilong Chen ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Infrared Image ◽  
Super Resolution ◽  
Fuzzy Cluster ◽  
Multi Scale ◽  
Image Super Resolution

scholarly journals Infrared Image Super-Resolution Reconstruction Based on Quaternion Fractional Order Total Variation with Lp Quasinorm

2018 ◽  
Vol 8 (10) ◽  
pp. 1864 ◽  
Author(s):  
Xingguo Liu ◽  
Yingpin Chen ◽  
Zhenming Peng ◽  
Juan Wu ◽  
Zhuoran Wang
Keyword(s):  
Total Variation ◽  
Fractional Order ◽  
Infrared Image ◽  
Super Resolution ◽  
Objective Evaluation ◽  
Imaging Systems ◽  
Infrared Images ◽  
Alternating Direction ◽  
Image Super Resolution ◽  
Staircase Artifacts

Owing to the limitations of the imaging principle as well as the properties of imaging systems, infrared images often have some drawbacks, including low resolution, a lack of detail, and indistinct edges. Therefore, it is essential to improve infrared image quality. Considering the information of neighbors, a description of sparse edges, and by avoiding staircase artifacts, a new super-resolution reconstruction (SRR) method is proposed for infrared images, which is based on fractional order total variation (FTV) with quaternion total variation and the L p quasinorm. Our proposed method improves the sparsity exploitation of FTV, and efficiently preserves image structures. Furthermore, we adopt the plug-and-play alternating direction method of multipliers (ADMM) and the fast Fourier transform (FFT) theory for the proposed method to improve the efficiency and robustness of our algorithm; in addition, an accelerated step is adopted. Our experimental results show that the proposed method leads to excellent performances in terms of an objective evaluation and the subjective visual effect.

scholarly journals Super-Resolution Network with Information Distillation and Multi-Scale Attention for Medical CT Image

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6870
Author(s):  
Tianliu Zhao ◽  
Lei Hu ◽  
Yongmei Zhang ◽  
Jianying Fang
Keyword(s):  
Super Resolution ◽  
Objective Evaluation ◽  
Learning Ability ◽  
Ct Image ◽  
Residual Network ◽  
Multi Scale ◽  
Series Of Experiments ◽  
Feature Information ◽  
Image Super Resolution ◽  
Feature Utilization

The CT image is an important reference for clinical diagnosis. However, due to the external influence and equipment limitation in the imaging, the CT image often has problems such as blurring, a lack of detail and unclear edges, which affect the subsequent diagnosis. In order to obtain high-quality medical CT images, we propose an information distillation and multi-scale attention network (IDMAN) for medical CT image super-resolution reconstruction. In a deep residual network, instead of only adding the convolution layer repeatedly, we introduce information distillation to make full use of the feature information. In addition, in order to better capture information and focus on more important features, we use a multi-scale attention block with multiple branches, which can automatically generate weights to adjust the network. Through these improvements, our model effectively solves the problems of insufficient feature utilization and single attention source, improves the learning ability and expression ability, and thus can reconstruct the higher quality medical CT image. We conduct a series of experiments; the results show that our method outperforms the previous algorithms and has a better performance of medical CT image reconstruction in the objective evaluation and visual effect.

scholarly journals X-raying the evolution of SN 1987A

2017 ◽  
Vol 12 (S331) ◽  
pp. 284-289
Author(s):  
Vinay L. Kashyap ◽  
David van Dyk ◽  
Katy McKeough ◽  
Frank Primini ◽  
Diab Jerius ◽  
...  
Keyword(s):  
Point Spread Function ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
X Ray ◽  
Multi Scale ◽  
Sn 1987A ◽  
Point Spread ◽  
Spread Function ◽  
High Resolution Images ◽  
Empirical Point

AbstractSN 1987A has been observed with the Chandra X-ray Observatory over the entire course of the mission. We have re-analyzed the archival data by constructing an empirical point spread function and reconstructing high-resolution images using a Bayesian multi-scale image reconstruction algorithm. We are able to resolve structure in the equatorial ring of SN 1987A with unprecedented detail, at scales of $\approx \frac{1}{4}$ arcsec. We describe how the point spread function is constructed, and the reconstruction method, and explore the evolution of the inner ring at different epochs and passbands.

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