Institution
Xidian University
Education•Xi'an, China•
About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.
Papers published on a yearly basis
Papers
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15 Oct 2019TL;DR: Zheng et al. as mentioned in this paper proposed a lightweight information multi-distillation network (IMDN) by constructing the cascaded information multidistillation blocks (IMDB), which contains distillation and selective fusion parts.
Abstract: In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive results. Thanks to the powerful representation capabilities of the deep networks, numerous previous ways can learn the complex non-linear mapping between low-resolution (LR) image patches and their high-resolution (HR) versions. However, excessive convolutions will limit the application of super-resolution technology in low computing power devices. Besides, super-resolution of any arbitrary scale factor is a critical issue in practical applications, which has not been well solved in the previous approaches. To address these issues, we propose a lightweight information multi-distillation network (IMDN) by constructing the cascaded information multi-distillation blocks (IMDB), which contains distillation and selective fusion parts. Specifically, the distillation module extracts hierarchical features step-by-step, and fusion module aggregates them according to the importance of candidate features, which is evaluated by the proposed contrast-aware channel attention mechanism. To process real images with any sizes, we develop an adaptive cropping strategy (ACS) to super-resolve block-wise image patches using the same well-trained model. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, memory footprint, and inference time. Code is available at \urlhttps://github.com/Zheng222/IMDN.
386 citations
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TL;DR: It is shown that ESPRIT exploits the invariance property of both the transmit array and the receive array in a bistatic MIMO radar to estimate the target's direction.
Abstract: It is shown that ESPRIT exploits the invariance property of both the transmit array and the receive array in a bistatic MIMO radar to estimate the target's direction. Some numerical results are presented to verify the effectiveness of this method.
384 citations
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TL;DR: The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, and then effectively extracts the object contour by labeling all the non-marker regions as either background or object.
381 citations
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07 Dec 2015TL;DR: It is demonstrated that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.
Abstract: Patch based image modeling has achieved a great success in low level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem. In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior. We demonstrate that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.
381 citations
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TL;DR: A modified ABC algorithm (denoted as ABC/best), which is based on that each bee searches only around the best solution of the previous iteration in order to improve the exploitation, is proposed.
380 citations
Authors
Showing all 32362 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Jie Zhang | 178 | 4857 | 221720 |
Bin Wang | 126 | 2226 | 74364 |
Huijun Gao | 121 | 685 | 44399 |
Hong Wang | 110 | 1633 | 51811 |
Jian Zhang | 107 | 3064 | 69715 |
Guozhong Cao | 104 | 694 | 41625 |
Lajos Hanzo | 101 | 2040 | 54380 |
Witold Pedrycz | 101 | 1766 | 58203 |
Lei Liu | 98 | 2041 | 51163 |
Qi Tian | 96 | 1030 | 41010 |
Wei Liu | 96 | 1538 | 42459 |
MengChu Zhou | 96 | 1124 | 36969 |
Chunying Chen | 94 | 508 | 30110 |
Daniel W. C. Ho | 85 | 360 | 21429 |