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Xin Tian

Bio: Xin Tian is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 10, co-authored 61 publications receiving 402 citations. Previous affiliations of Xin Tian include Huazhong University of Science and Technology.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a comprehensive review and analysis of latest deep learning methods in different image fusion scenarios is provided, and the evaluation for some representative methods in specific fusion tasks are performed qualitatively and quantitatively.

153 citations

Journal ArticleDOI
TL;DR: The key ideas of the proposed method are to use the directional support value of Gaussian transform (DSVoGT) to enhance the targets, and use the multiscale representation provided by DSVoGT to reduce the false alarm rate.
Abstract: Robust small target detection is one of the key techniques in IR search and tracking systems for self-defense or attacks. In this paper we present a robust solution for small target detection in a single IR image. The key ideas of the proposed method are to use the directional support value of Gaussian transform (DSVoGT) to enhance the targets, and use the multiscale representation provided by DSVoGT to reduce the false alarm rate. The original image is decomposed into sub-bands in different orientations by convolving the image with the directional support value filters, which are deduced from the weighted mapped least-squares–support vector machines (LS–SVMs). Based on the sub-band images, a support value of Gaussian matrix is constructed, and the trace of this matrix is then defined as the target measure. The corresponding multiscale correlations of the target measures are computed for enhancing target signal while suppressing the background clutter. We demonstrate the advantages of the proposed method on real IR images and compare the results against those obtained from standard detection approaches, including the top-hat filter, max–mean filter, max–median filter, min–local–Laplacian of Gaussian (LoG) filter, as well as LS–SVM. The experimental results on various cluttered background images show that the proposed method outperforms other detectors.

71 citations

Journal ArticleDOI
TL;DR: An efficient multi-input/multi-output VLSI architecture for two-dimensional lifting-based discrete wavelet transform (DWT) with computing time as low as N2/M for an N × N image with controlled increase of hardware cost is proposed.
Abstract: This brief paper proposes an efficient multi-input/multi-output VLSI architecture (MIMOA) for two-dimensional lifting-based discrete wavelet transform (DWT). The novelty is the simplicity and generality to construct the MIMOA, which is a high-speed architecture with computing time as low as N2/M for an N × N image with controlled increase of hardware cost. M is the throughput rate.

63 citations

Journal ArticleDOI
TL;DR: This study proposes a variational pansharpening method by exploiting cartoon-texture similarities by incorporating a data fidelity term for preserving the spectral information on the basis that the down-sampled fused MS image is consistent with the MS image, and formulate panshARPening as an optimization problem and solve it efficiently using the alternative direction multiplier method.
Abstract: Pansharpening aims to fuse a multispectral (MS) image with low spatial resolution and a panchromatic (PAN) image with a high-spatial resolution to produce an image with both high spectral and high spatial resolution. In this study, we propose a variational pansharpening method by exploiting cartoon-texture similarities. After decomposition of the PAN image, the cartoon component always contains the global structure information, while the texture component includes the locally patterned information. This enables that the fused high-spatial resolution MS image can preserve the global and local spatial details (e.g., high-order information) well after leveraging the similarities of cartoon and texture components from PAN and MS images. To explore such cartoon-texture similarities, we describe cartoon similarity as gradient sparsity, formulated as a reweighted total variation term. Meanwhile, we use group low-rank constraint for texture similarity that is presented as repetitive texture patterns. By incorporating a data fidelity term for preserving the spectral information on the basis that the down-sampled fused MS image is consistent with the MS image, we further formulate pansharpening as an optimization problem and solve it efficiently using the alternative direction multiplier method. Extensive experiments have been conducted on a series of satellite data sets, and we also carry out a simulated vegetation coverage change experiment to verify the efficiency of the proposed method in remote sensing. The qualitative and quantitative results demonstrate that our method outperforms the state-of-the-art pansharpening methods in terms of both visual effect and objective metrics.

48 citations

Journal ArticleDOI
TL;DR: A variational pansharpening method based on gradient sparse representation is proposed, based on the observation that the gradients of corresponding MS and PAN images with different resolutions have the similar sparse coefficients under certain specific dictionaries.
Abstract: By exploiting the gradient similarity between multispectral (MS) and panchromatic (PAN) images, a variational pansharpening method based on gradient sparse representation is proposed, based on the observation that the gradients of corresponding MS and PAN images with different resolutions have the similar sparse coefficients under certain specific dictionaries. By adding a data fidelity term to preserve the spectral information, an optimization model is constructed as a minimization problem of an energy function. The problem can be solved by the gradient descent method efficiently. Experiments on different satellite data reveal that the proposed method outperforms the state-of-the-art methods in terms of visual effect and objective quality analysis.

43 citations


Cited by
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Journal ArticleDOI
TL;DR: Experimental results on three sequences demonstrate that the proposed small target detection method can not only suppress background clutter effectively even if with strong noise interference, but also detect targets accurately with low false alarm rate and high speed.

400 citations

Posted Content
TL;DR: A multi-granularity masked face recognition model is developed that achieves 95% accuracy, exceeding the results reported by the industry and is currently the world's largest real-world masked face dataset.
Abstract: In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depend on a large number of face samples. However, at present, there are no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD). Among them, to the best of our knowledge, RMFRD is currently theworld's largest real-world masked face dataset. These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed. The multi-granularity masked face recognition model we developed achieves 95% accuracy, exceeding the results reported by the industry. Our datasets are available at: this https URL.

277 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed multiscale detection algorithm can deal with different sizes of small targets under complex backgrounds and has a better effectiveness and robustness against existing algorithms.
Abstract: Infrared (IR) small target detection with high detection rate, low false alarm rate, and high detection speed has a significant value, but it is usually very difficult since the small targets are usually very dim and may be easily drowned in different types of interferences. Current algorithms cannot effectively enhance real targets and suppress all the types of interferences simultaneously. In this letter, a multiscale detection algorithm utilizing the relative local contrast measure (RLCM) is proposed. It has a simple structure: first, the multiscale RLCM is calculated for each pixel of the raw IR image to enhance real targets and suppress all the types of interferences simultaneously; then, an adaptive threshold is applied to extract real targets. Experimental results show that the proposed algorithm can deal with different sizes of small targets under complex backgrounds and has a better effectiveness and robustness against existing algorithms. Besides, the proposed algorithm has the potential of parallel processing, which is very useful for improving the detection speed.

235 citations

Journal ArticleDOI
TL;DR: This survey provides a comprehensive review of multimodal image matching methods from handcrafted to deep methods for each research field according to their imaging nature, including medical, remote sensing and computer vision.

155 citations

Journal ArticleDOI
TL;DR: In this paper, a comprehensive review and analysis of latest deep learning methods in different image fusion scenarios is provided, and the evaluation for some representative methods in specific fusion tasks are performed qualitatively and quantitatively.

153 citations