Polarized image super-resolution via a deep convolutional neural network

Hu, Haofeng; Yang, Shi‐Yao; Li, Xiaobo; Cheng, Zhenzhou; Liu, T.; Zhai, Jingsheng

doi:10.1364/oe.479700

Cited by 15 publications

(6 citation statements)

References 27 publications

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“…Deep-learning-based P-lidar techniques: Deep learning technology, with its nonlinear convolution operations and powerful implicit correlation learning, leverages the advantages of data-driven approaches to enhance performance in various tasks related to polarization or lidar [204][205][206][207][208][209]. Compared to traditional intensity-based lidar, P-lidar offers additional information, including time-of-flight data and polarization information.…”

Section: Discussionmentioning

confidence: 99%

Polarization Lidar: Principles and Applications

Liu,

Zhang,

Zhai

et al. 2023

Photonics

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Traditional lidar techniques mainly rely on the backscattering/echo light intensity and spectrum as information sources. In contrast, polarization lidar (P-lidar) expands the dimensions of detection by utilizing the physical property of polarization. By incorporating parameters such as polarization degree, polarization angle, and ellipticity, P-lidar enhances the richness of physical information obtained from target objects, providing advantages for subsequent information analysis. Over the past five decades, the application fields of P-lidar have rapidly expanded, starting from its early use in atmospheric remote sensing to later applications in oceanic remote sensing. This review first provides a brief introduction to the basic principles of both polarization and P-lidar, along with typical systems. It then explores the applications of P-lidar in various remote sensing fields, including atmospheric, oceanic, and terrestrial domains. Additionally, we propose potential research directions based on current cutting-edge applications, with the aims of providing critical insights to researchers in the fields of polarization and lidar and inspiring further exciting ideas.

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Section: Discussionmentioning

confidence: 99%

Polarization Lidar: Principles and Applications

Liu,

Zhang,

Zhai

et al. 2023

Photonics

Self Cite

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“…In response to these challenges, researchers have introduced novel methods utilizing deep convolutional neural networks for super-resolution reconstruction. Hu et al [27] modified the network architecture to simplify performance and training. Another method enhances the correlation between neighboring feature information and overall image quality by incorporating context information into the net-work.…”

Section: Literature Reviewmentioning

confidence: 99%

E-SEVSR—Edge Guided Stereo Endoscopic Video Super-Resolution

Hayat,

Aramvith

2024

IEEE Access

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Integrating Stereo Imaging technology into medical diagnostics and surgeries marks a significant revolution in medical sciences. This advancement gives surgeons and physicians a deeper understanding of patients' organ anatomy. However, like any technology, stereo cameras have their limitations, such as low resolution (LR) and output images that are often blurry. Our paper introduces a novel approach-a multi-stage network with a pioneering Stereo Endoscopic Attention Module (SEAM). This network aims to progressively enhance the quality of super-resolution (SR), moving from coarse to fine details. Specifically, we propose an edge-guided stereo attention mechanism integrated into each interaction of stereo features. This mechanism aims to capture consistent structural details across different views more effectively. Our proposed model demonstrates superior super-resolution reconstruction performance through comprehensive quantitative evaluations and experiments conducted on three datasets. Our E-SEVSR framework demonstrates superiority over alternative approaches. This framework leverages the edge-guided stereo attention mechanism within the multi-stage network, improving super-resolution quality in medical imaging applications.

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“…Recent advances have utilized deep convolutional neural networks for SR reconstruction. Hu et al [16] refined the network architecture to enhance performance and simplify training. Other methods have focused on improving feature relationships and overall image quality by incorporating context into the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Saliency-Aware Deep Learning Approach for Enhanced Endoscopic Image Super-Resolution

Hayat,

Aramvith

2024

IEEE Access

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The adoption of Stereo Imaging technology within endoscopic procedures represents a transformative advancement in medical imaging, providing surgeons with depth perception and detailed views of internal anatomy for enhanced diagnostic accuracy and surgical precision. However, the practical application of stereo imaging in endoscopy faces challenges, including the generation of low-resolution and blurred images, which can hinder the effectiveness of medical diagnoses and interventions. Our research introduces an endoscopic image SR model in response to these specific. This model features an innovative feature extraction module and an advanced cross-view feature interaction module tailored for the intricacies of endoscopic imagery. Initially trained on the SCARED dataset, our model was rigorously tested across four additional publicly available endoscopic image datasets at scales 2, 4, and 8, demonstrating unparalleled performance improvements in endoscopic SR. Our results are compelling. They show that our model not only substantially enhances the quality of endoscopic images but also consistently surpasses other existing methods like E-SEVSR [6], DCCSRNet [10], and CCSBESR [5] in all tested datasets, in quantitative measures such as PSNR and SSIM, and in qualitative evaluations. The successful application of our SR model in endoscopic imaging has the potential to revolutionize medical diagnostics and surgery, significantly increasing the precision and effectiveness of endoscopic procedures. The code will be released on GitHub and can be accessed at https://github.com/cu-vtrg-lab/ Saliency-Aware-Deep-Learning-Approach-for-Enhanced-Endoscopic-Image-SR.

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Polarized image super-resolution via a deep convolutional neural network

Cited by 15 publications

References 27 publications

Polarization Lidar: Principles and Applications

Polarization Lidar: Principles and Applications

E-SEVSR—Edge Guided Stereo Endoscopic Video Super-Resolution

Saliency-Aware Deep Learning Approach for Enhanced Endoscopic Image Super-Resolution

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