Mu Shaoshuo, Zhang Jiefang. An anisotropic edge total generalized variation energy super-resolution based on fast l1-norm dictionary edge representations[J]. Opto-Electronic Engineering, 2019, 46(11): 180499. doi: 10.12086/oee.2019.180499
Citation: Mu Shaoshuo, Zhang Jiefang. An anisotropic edge total generalized variation energy super-resolution based on fast l1-norm dictionary edge representations[J]. Opto-Electronic Engineering, 2019, 46(11): 180499. doi: 10.12086/oee.2019.180499

An anisotropic edge total generalized variation energy super-resolution based on fast l1-norm dictionary edge representations

    Fund Project: Supported by National Natural Science Foundation of China (61877053) and General Scientific Research Projects of Zhejiang Education Department (Y201840087)
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  • For camera-based imaging, low resolution and noise outliers are the major challenges. Here, we propose a novel super-resolution method-total generalized variation (TGV) super-resolution based on fast l1-norm dictionary edge representations. First, anisotropic diffusion tensor (ADT) is utilized as high frequency edge information. The fast l1-norm dictionary representation method is used to create dictionaries of LR image and the corresponding high frequency edge information. This method can quickly build dictionaries on the same database, and avoid the influence of outliers. Then we combine the edge information ADT and TGV model as the new regularization function. Finally, the super-resolution cost function is established. The results show that the algorithm has high feasibility and robustness to simulation data and SO12233 target data. It can effectively remove noise outliers and obtain high-quality clear images. Compared with other classical algorithms, the proposed algorithm can obtain higher PSNR and SSIM values.
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  • Overview:Optical cameras of remote sensing, aviation and other reconnaissance equipment are taken as the application background. Aiming at the problems of low resolution imaging and serious noise interference, a high-precision super-resolution method with strong robustness to noise is studied. In this paper, a novel super-resolution method-total generalized variation (TGV) super-resolution based on fast l1-norm dictionary edge representations is proposed. The proposed method is analyzed and compared with the conventional methods through several experiments. On the whole, this method is superior to other classical methods. It has high feasibility and robustness to simulation data and SO12233 target data. Furthermore, it can effectively remove noise outliers and obtain high-quality reconstruction image.

    Detector is an important part of optical camera, and its discrete sampling and optical system speckle are the main factors affecting its imaging resolution. Firstly, the detector sampling is mainly reflected in the fact that in order to meet the SNR requirements, the Nyquist frequency of the imaging system should be lower than the cutoff frequency of the optical system, but it will cause frequency aliasing. Secondly, the speckle of optical system leads to point spread effect due to diffraction. Both of them will lead to low resolution and poor quality of camera imaging, which will affect reconnaissance. At the same time, the noise introduced in the acquisition of target scene by optical system is also an important factor affecting the imaging quality.

    In order to solve the above problems, the proposed method combines sparse representation with generalized total variation for targeted improvement and innovation. First, anisotropic diffusion tensor (ADT) is utilized as high frequency edge information. The fast l1-norm dictionary representation method is used to create dictionaries of LR image and the corresponding high frequency edge information. This method can quickly build dictionaries on the same database, and avoid the influence of outliers. Then we combine the edge information ADT and TGV model as the new regularization function. Finally, the super-resolution cost function is established. In the experimental part, simulation data and real SO12233 target data are used to prove the effectiveness of the algorithm in this paper, which can not only remove LR noise outliers, but also produce more accurate reconstructions than those produced by other general super-resolution algorithms. Meanwhile, the proposed algorithm can obtain higher PSNR and SSIM values.

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