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 Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage

2020 ◽  
Vol 10 (7) ◽  
pp. 2533 ◽  
Author(s):  
Jingjing Yang ◽  
Yingpin Chen ◽  
Zhifeng Chen
Keyword(s):  
Total Variation ◽  
Infrared Image ◽  
Noise Pollution ◽  
Image Deblurring ◽  
High Order ◽  
L1 Norm ◽  
Alternating Direction ◽  
Image Blurring ◽  
Image Gradients ◽  
Staircase Artifacts

The quality of infrared images is affected by various degradation factors, such as image blurring and noise pollution. Anisotropic total variation (ATV) has been shown to be a good regularization approach for image deblurring. However, there are two main drawbacks in ATV. First, the conventional ATV regularization just considers the sparsity of the first-order image gradients, thus leading to staircase artifacts. Second, it employs the L1-norm to describe the sparsity of image gradients, while the L1-norm has a limited capacity of depicting the sparsity of sparse variables. To address these limitations of ATV, a high-order total variation is introduced in the ATV deblurring model and the Lp-pseudonorm is adopted to depict the sparsity of low- and high-order total variation. In this way, the recovered image can fit the image priors with clear edges and eliminate the staircase artifacts of the ATV model. The alternating direction method of multipliers is used to solve the proposed model. The experimental results demonstrate that the proposed method does not only remove blurs effectively but is also highly competitive against the state-of-the-art methods, both qualitatively and quantitatively.

scholarly journals Infrared Image Super-Resolution via Progressive Compact Distillation Network

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3107
Author(s):  
Kefeng Fan ◽  
Kai Hong ◽  
Fei Li
Keyword(s):  
Transfer Learning ◽  
Learning Strategy ◽  
Infrared Image ◽  
Super Resolution ◽  
Infrared Images ◽  
Deep Convolutional Neural Networks ◽  
Calculation Algorithm ◽  
Reconstruction Performance ◽  
Image Super Resolution ◽  
Public Datasets

Deep convolutional neural networks are capable of achieving remarkable performance in single-image super-resolution (SISR). However, due to the weak availability of infrared images, heavy network architectures for insufficient infrared images are confronted by excessive parameters and computational complexity. To address these issues, we propose a lightweight progressive compact distillation network (PCDN) with a transfer learning strategy to achieve infrared image super-resolution reconstruction with a few samples. We design a progressive feature residual distillation (PFDB) block to efficiently refine hierarchical features, and parallel dilation convolutions are utilized to expand PFDB’s receptive field, thereby maximizing the characterization power of marginal features and minimizing the network parameters. Moreover, the bil-global connection mechanism and the difference calculation algorithm between two adjacent PFDBs are proposed to accelerate the network convergence and extract the high-frequency information, respectively. Furthermore, we introduce transfer learning to fine-tune network weights with few-shot infrared images to obtain infrared image mapping information. Experimental results suggest the effectiveness and superiority of the proposed framework with low computational load in infrared image super-resolution. Notably, our PCDN outperforms existing methods on two public datasets for both ×2 and ×4 with parameters less than 240 k, proving its efficient and excellent reconstruction performance.

scholarly journals Infrared Image Super Resolution by Combining Compressive Sensing and Deep Learning

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2587 ◽  
Author(s):  
Xudong Zhang ◽  
Chunlai Li ◽  
Qingpeng Meng ◽  
Shijie Liu ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Compressive Sensing ◽  
High Frequency ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Super Resolution ◽  
Infrared Images ◽  
Frequency Information ◽  
Image Super Resolution

Super resolution methods alleviate the high cost and high difficulty in applying high resolution infrared image sensors. In this paper we present a novel single image super resolution method for infrared images by combining compressive sensing theory and deep learning. Low resolution images can be regarded as the compressed sampling results of the high resolution ones in compressive sensing. With sparsity in this theory, higher resolution images can be reconstructed. However, because of diverse level of sparsity for different images, the output contains noise and loss of high frequency information. Deep convolutional neural network provides a solution to relieve the noise and supplement some missing high frequency information. By concatenating two methods, we manage to produce better results in super resolution tasks for infrared images than SRCNN and ScSR. PSNR and SSIM values are used to quantify the performance. Applying our method to open datasets and actual infrared imaging experiments, we also find better visual results are preserved.

scholarly journals Low-Rank Total Variation for Image Super-Resolution

2013 ◽  
pp. 155-162 ◽  
Author(s):  
Feng Shi ◽  
Jian Cheng ◽  
Li Wang ◽  
Pew-Thian Yap ◽  
Dinggang Shen
Keyword(s):  
Total Variation ◽  
Super Resolution ◽  
Low Rank ◽  
Image Super Resolution
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