Infrared Image Super-Resolution via Transfer Learning and PSRGAN

Huang, Yongsong; Jiang, Zetao; Lan, Rushi; Zhang, Shao-Qin; Pi, Kui

doi:10.1109/lsp.2021.3077801

Cited by 41 publications

(17 citation statements)

References 23 publications

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“…Chen et al [51] employed an iterative error reconstruction mechanism to perform SR in a coarse-to-fine manner. Huang et al [52] proposed a progressive super-resolution generative adversarial network and employed the multistage transfer learning strategy to improve the SR performance from small samples. Prajapati et al [53] proposed channel splitting-based convolutional neural network to eliminate the redundant features for efficient inference.…”

Section: Infrared Image Srmentioning

confidence: 99%

“…In this subsection, we compare our MoCoPnet with 1 topperforming single image SR methods RCAN [15], 5 video SR methods VSRnet [75], VESPCN [34], SOF-VSR [35] and TDAN [39], D3Dnet [30] and 3 infrared image SR methods IERN [51], PSRGAN [52], and ChaSNet [53]. For fair comparison, we retrain all the compared methods on infrared small target dataset [70] and exclude the first and the last 2 frames of the video sequences for performance evaluation.…”

Section: Comparative Evaluationmentioning

confidence: 99%

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Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution

Ying

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Infrared small target super-resolution (SR) aims to recover reliable and detailed high-resolution image with highcontrast targets from its low-resolution counterparts. Since the infrared small target lacks color and fine structure information, it is significant to exploit the supplementary information among sequence images to enhance the target. In this paper, we propose the first infrared small target SR method named local motion and contrast prior driven deep network (MoCoPnet) to integrate the domain knowledge of infrared small target into deep network, which can mitigate the intrinsic feature scarcity of infrared small targets. Specifically, motivated by the local motion prior in the spatio-temporal dimension, we propose a local spatiotemporal attention module to perform implicit frame alignment and incorporate the local spatio-temporal information to enhance the local features (especially for small targets). Motivated by the local contrast prior in the spatial dimension, we propose a central difference residual group to incorporate the central difference convolution into the feature extraction backbone, which can achieve center-oriented gradient-aware feature extraction to further improve the target contrast. Extensive experiments have demonstrated that our method can recover accurate spatial dependency and improve the target contrast. Comparative results show that MoCoPnet can outperform the state-of-the-art video SR and single image SR methods in terms of both SR performance and target enhancement. Based on the SR results, we further investigate the influence of SR on infrared small target detection and the experimental results demonstrate that MoCoPnet promotes the detection performance. The code is available at https://github.com/XinyiYing/MoCoPnet.

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Section: Infrared Image Srmentioning

confidence: 99%

Section: Comparative Evaluationmentioning

confidence: 99%

Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution

Ying

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Chen [29] et al exploited different transfer learning implementations, i.e., pretrained deep learning models combined with support vector machine (SVM) and fine-tuning, in detecting unfavorable driving states. In the infrared image domain, PSRGAN [30] employs a multistage transfer learning strategy utilizing visible images and 100 infrared images to boost the restoration performance of infrared images. In this paper, the proposed PCDN only employs 55 infrared images to fine-tune the pretrained network and performs better compared to existing SR approaches.…”

Section: Infrared Image Sr and Transfer Learningmentioning

confidence: 99%

Infrared Image Super-Resolution via Progressive Compact Distillation Network

Fan

Hong

2021

Electronics

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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.

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“…First, the two basic types of statistical noise -Poisson and Gaussian-are common in radiographic images [9,27]. However, most of the existing state-of-the-art deep learning-based SR methods focus only on the degradation of bicubic downsampling [12,16,21], i.e., the models directly take the bicubic downsampling images as input and reconstruct the HR images, leading to the problem of domain shift when applied to noisy images. Second, the disruptions in the application scenarios arise from the following factors: radiologists and patients, as well as information loss due to compressed transmission via the Internet [6,19].…”

Section: Introductionmentioning

confidence: 99%

“…To address this problem, more attention has been paid to the tasks of medical image super-resolution [7,18,25]. Deep learning-based methods [12,16,29,33,34] dominate image SR, which learns a mapping from LR images to HR images and differs from traditional methods in which more prior knowledge is required [3]. Recently, blind SR that considers real-world degradation has drawn much attention [28,30,33,34] because it is more common.…”

Section: Introductionmentioning

confidence: 99%

Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN

Wang¹,

Omachi²

2022

Preprint

Self Cite

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In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Furthermore, we propose AID-SRGAN, which can simultaneously denoise and generate high-resolution (HR) radiographs. In this model, we introduce an attention mechanism into the denoising module to make it more robust to complicated degradation. Finally, the SR module reconstructs the HR radiographs using the "clean" low-resolution (LR) radiographs. In addition, we propose a separate-joint training approach to train the model, and extensive experiments are conducted to show that the proposed method is superior to its counterparts. e.g., our proposed method achieves 31.90 of PSNR with a scale factor of 4×, which is 7.05% higher than that obtained by recent work, SPSR [16]. Our dataset and code will be made available at: https://github.com/yongsongH/AIDSRGAN-MICCAI2022.

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Infrared Image Super-Resolution via Transfer Learning and PSRGAN

Cited by 41 publications

References 23 publications

Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution

Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution

Infrared Image Super-Resolution via Progressive Compact Distillation Network

Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN

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