Samuel W. Hasinoff

22 papers receiving 1.9k citations

Hit Papers

Deep bilateral learning for real-time image enhancement201620262019202220172016100200300400500

Peers

Samuel W. Hasinoff
Comparison fields: 5 of 99
  • Computer Vision and Pattern Recognition 1.7k
  • Media Technology 749
  • Atomic and Molecular Physics, and Optics 166
  • Computer Graphics and Computer-Aided Design 146
  • Biomedical Engineering 145
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Eino-Ville Talvala United States
Chia‐Kai Liang Taiwan
Moshe Ben-Ezra China
Vaibhav Vaish United States
Xiaodong Cun China
Yacov Hel-Or Israel
Wei‐Sheng Lai United States
Keigo Hirakawa United States
Jonas Unger Sweden
Yuhui Quan China
Samuel W. Hasinoff relative to Eino-Ville Talvala United States Eino-Ville Talvala's profile →
Citations per field
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Citations per year

Countries citing papers authored by Samuel W. Hasinoff

Since Specialization
Citations

This map shows the geographic impact of Samuel W. Hasinoff's research. 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 Samuel W. Hasinoff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel W. Hasinoff more than expected).

Fields of papers citing papers by Samuel W. Hasinoff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuel W. Hasinoff. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Samuel W. Hasinoff. The network helps show where Samuel W. Hasinoff may publish in the future.

Co-authorship network of co-authors of Samuel W. Hasinoff

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel W. Hasinoff. A scholar is included among the top collaborators of Samuel W. Hasinoff based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Samuel W. Hasinoff. Samuel W. Hasinoff is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Deep bilateral learning for real-time image enhancementbreakdown →
514
2 79
3
Burst photography for high dynamic range and low-light imaging on mobile camerasbreakdown →
276
4 92
5 142
6 23
7 13
8 44
9
Fast and Robust Pyramid-based Image Processing
13
10 7
11 233
12 154
13 9
14 44
15 87
16 44
17 40
18 47
19 25
20
Photo-Consistent 3D Fire by Flame-Sheet Decomposition
12

About Samuel W. Hasinoff

Samuel W. Hasinoff is a scholar working on Computer Graphics and Computer-Aided Design, Media Technology and Computer Vision and Pattern Recognition, having authored 22 papers that have together received 1.9k indexed citations. Recurring topics across this work include Advanced Vision and Imaging (13 papers), Image Enhancement Techniques (10 papers) and Image Processing Techniques and Applications (8 papers). The work is most often cited by research in Media Technology (749 citations), Computer Vision and Pattern Recognition (1.7k citations) and Computer Graphics and Computer-Aided Design (146 citations). Samuel W. Hasinoff has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Frédo Durand, Jan Kautz, Sylvain Paris, Jonathan T. Barron, Jiawen Chen, Kiriakos N. Kutulakos, Michaël Gharbi, William T. Freeman, Andrew Adams and Marc Levoy. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Communications of the ACM and ACM Transactions on Graphics.

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.

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