Natural Image Denoising with Convolutional Networks

476 indexed citations

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This paper, published in 2008, received 476 indexed citations. Written by Viren Jain and Sebastian Seung 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 (371 citations), Media Technology (204 citations) and Biomedical Engineering (44 citations). Published in Neural Information Processing Systems.

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Countries where authors are citing Natural Image Denoising with Convolutional Networks

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This map shows the geographic impact of Natural Image Denoising with Convolutional Networks. 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 Natural Image Denoising with Convolutional Networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Natural Image Denoising with Convolutional Networks more than expected).

Fields of papers citing Natural Image Denoising with Convolutional Networks

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Natural Image Denoising with Convolutional Networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Natural Image Denoising with Convolutional Networks.

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/w3158083.

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