• Title/Summary/Keyword: Multi-frame SR

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Multi-Frame Super-Resolution of High Frequency with Spatially Weighted Bilateral Total Variance Regularization

  • Lee, Oh-Young;Park, Sae-Jin;Kim, Jae-Woo;Kim, Jong-Ok
    • IEIE Transactions on Smart Processing and Computing
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    • v.3 no.5
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    • pp.271-274
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    • 2014
  • Bayesian based Multi-Frame Super-Resolution (MF-SR) has been used as a popular and effective SR model. On the other hand, the texture region is not reconstructed sufficiently because it works on the spatial domain. In this study, the MF-SR method was extended to operate on the frequency domain to improve HF information as much as possible. For this, a spatially weighted bilateral total variation model was proposed as a regularization term for a Bayesian estimation. The experimental results showed that the proposed method can recover the texture region more realistically with reduced noise, compared to conventional methods.

Multi-feature local sparse representation for infrared pedestrian tracking

  • Wang, Xin;Xu, Lingling;Ning, Chen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.3
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    • pp.1464-1480
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    • 2019
  • Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

UHD TV Image Enhancement using Multi-frame Example-based Super-resolution (멀티프레임 예제기반 초해상도 영상복원을 이용한 UHD TV 영상 개선)

  • Jeong, Seokhwa;Yoon, Inhye;Paik, Joonki
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.3
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    • pp.154-161
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    • 2015
  • A novel multiframe super-resolution (SR) algorithm is presented to overcome the limitation of existing single-image SR algorithms using motion information from adjacent frames in a video. The proposed SR algorithm consists of three steps: i) definition of a local region using interframe motion vectors, ii) multiscale patch generation and adaptive selection of multiple optimum patches, and iii) combination of optimum patches for super-resolution. The proposed algorithm increases the accuracy of patch selection using motion information and multiscale patches. Experimental results show that the proposed algorithm performs better than existing patch-based SR algorithms in the sense of both subjective and objective measures including the peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM).