Deep Convolutional Neural Network for Image Deconvolution

534 indexed citations

Abstract

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About

This paper, published in 2014, received 534 indexed citations. Written by Xu Li, Jimmy Ren, Ce Liu and Jiaya Jia covering the research area of Media Technology and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (405 citations), Media Technology (201 citations) and Biomedical Engineering (56 citations). Published in Rare & Special e-Zone (The Hong Kong University of Science and Technology).

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Countries where authors are citing Deep Convolutional Neural Network for Image Deconvolution

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Citations

This map shows the geographic impact of Deep Convolutional Neural Network for Image Deconvolution. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Deep Convolutional Neural Network for Image Deconvolution with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep Convolutional Neural Network for Image Deconvolution more than expected).

Fields of papers citing Deep Convolutional Neural Network for Image Deconvolution

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Deep Convolutional Neural Network for Image Deconvolution. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep Convolutional Neural Network for Image Deconvolution.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w13007089.

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