The Experts below are selected from a list of 56706 Experts worldwide ranked by ideXlab platform

Pradip K Srimani - One of the best experts on this subject based on the ideXlab platform.

Stephen T Hedetniemi - One of the best experts on this subject based on the ideXlab platform.

Zhiying Liang - One of the best experts on this subject based on the ideXlab platform.

  • self stabilizing minimum spanning tree construction on message passing Networks
    International Symposium on Distributed Computing, 2001
    Co-Authors: Lisa Higham, Zhiying Liang
    Abstract:

    Self-stabilizing algorithms for constructing a spanning tree of an Arbitrary Network have been studied for many models of distributed Networks including those that communicate via registers (either composite or read/write atomic) and those that employ message-passing. In contrast, much less has been done for the corresponding minimum spanning tree problem. The one published self-stabilizing distributed algorithm for the minimum spanning problem that we are aware of [3] assumes a composite atomicity model. This paper presents two minimum spanning tree algorithms designed directly for deterministic, message-passing Networks. The first converts an Arbitrary spanning tree to a minimum one; the second is a fully self-stabilizing construction. The algorithms assume distinct identifiers and reliable fifo message passing, but do not rely on a root or synchrony. Also, processors have a safe time-out mechanism (the minimum assumption necessary for a solution to exist.) Both algorithms apply to Networks that can change dynamically.

Wayne Goddard - One of the best experts on this subject based on the ideXlab platform.

Jian Sun - One of the best experts on this subject based on the ideXlab platform.

  • meta sr a magnification Arbitrary Network for super resolution
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun
    Abstract:

    Recent research on super-resolution has achieved great success due to the development of deep convolutional neural Networks (DCNNs). However, super-resolution of Arbitrary scale factor has been ignored for a long time. Most previous researchers regard super-resolution of different scale factors as independent tasks. They train a specific model for each scale factor which is inefficient in computing, and prior work only take the super-resolution of several integer scale factors into consideration. In this work, we propose a novel method called Meta-SR to firstly solve super-resolution of Arbitrary scale factor (including non-integer scale factors) with a single model. In our Meta-SR, the Meta-Upscale Module is proposed to replace the traditional upscale module. For Arbitrary scale factor, the Meta-Upscale Module dynamically predicts the weights of the upscale filters by taking the scale factor as input and use these weights to generate the HR image of Arbitrary size. For any low-resolution image, our Meta-SR can continuously zoom in it with Arbitrary scale factor by only using a single model. We evaluated the proposed method through extensive experiments on widely used benchmark datasets on single image super-resolution. The experimental results show the superiority of our Meta-Upscale.

  • CVPR - Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
    2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
    Co-Authors: Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun
    Abstract:

    Recent research on super-resolution has achieved greatsuccess due to the development of deep convolutional neu-ral Networks (DCNNs). However, super-resolution of arbi-trary scale factor has been ignored for a long time. Mostprevious researchers regard super-resolution of differentscale factors as independent tasks. They train a specificmodel for each scale factor which is inefficient in comput-ing, and prior work only take the super-resolution of sev-eral integer scale factors into consideration. In this work,we propose a novel method called Meta-SR to firstly solvesuper-resolution of Arbitrary scale factor (including non-integer scale factors) with a single model. In our Meta-SR,the Meta-Upscale Module is proposed to replace the tradi-tional upscale module. For Arbitrary scale factor, the Meta-Upscale Module dynamically predicts the weights of the up-scale filters by taking the scale factor as input and use theseweights to generate the HR image of Arbitrary size. For anylow-resolution image, our Meta-SR can continuously zoomin it with Arbitrary scale factor by only using a single model.We evaluated the proposed method through extensive exper-iments on widely used benchmark datasets on single imagesuper-resolution. The experimental results show the superi-ority of our Meta-Upscale.