Mikhail Uss

510 total citations
23 papers, 271 citations indexed

About

Mikhail Uss is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Aerospace Engineering. According to data from OpenAlex, Mikhail Uss has authored 23 papers receiving a total of 271 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 17 papers in Media Technology and 4 papers in Aerospace Engineering. Recurrent topics in Mikhail Uss's work include Image and Signal Denoising Methods (15 papers), Advanced Image Fusion Techniques (12 papers) and Remote-Sensing Image Classification (10 papers). Mikhail Uss is often cited by papers focused on Image and Signal Denoising Methods (15 papers), Advanced Image Fusion Techniques (12 papers) and Remote-Sensing Image Classification (10 papers). Mikhail Uss collaborates with scholars based in Ukraine, France and Finland. Mikhail Uss's co-authors include Benoît Vozel, Kacem Chehdi, Vladimir Lukin, Sergey Abramov, Karen Egiazarian, Nikolay Ponomarenko, Oleksiy Pogrebnyak, Jaakko Astola, Mo Zhang and Nataliia Kussul and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, Sensors and Remote Sensing.

In The Last Decade

Mikhail Uss

21 papers receiving 265 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mikhail Uss Ukraine 9 208 177 37 33 15 23 271
Doochun Seo South Korea 9 191 0.9× 158 0.9× 51 1.4× 25 0.8× 7 0.5× 37 300
Lirong Han Spain 10 175 0.8× 207 1.2× 29 0.8× 64 1.9× 10 0.7× 22 304
V. Tsagaris Greece 9 161 0.8× 208 1.2× 104 2.8× 20 0.6× 5 0.3× 20 282
Qi Hu China 8 235 1.1× 139 0.8× 114 3.1× 13 0.4× 25 1.7× 16 326
Xianyu Jin China 5 217 1.0× 151 0.9× 32 0.9× 16 0.5× 9 0.6× 10 313
Hong-Xia Dou China 6 187 0.9× 240 1.4× 20 0.5× 29 0.9× 24 1.6× 24 289
Pei Xiang China 9 147 0.7× 326 1.8× 33 0.9× 98 3.0× 18 1.2× 22 361
Myungjin Choi South Korea 5 236 1.1× 305 1.7× 49 1.3× 15 0.5× 7 0.5× 11 438
Chein‐I Chang United States 8 95 0.5× 156 0.9× 32 0.9× 71 2.2× 19 1.3× 16 270

Countries citing papers authored by Mikhail Uss

Since Specialization
Citations

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

Fields of papers citing papers by Mikhail Uss

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mikhail Uss. 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 Mikhail Uss. The network helps show where Mikhail Uss may publish in the future.

Co-authorship network of co-authors of Mikhail Uss

This figure shows the co-authorship network connecting the top 25 collaborators of Mikhail Uss. A scholar is included among the top collaborators of Mikhail Uss 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 Mikhail Uss. Mikhail Uss 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
1.
Uss, Mikhail, Benoît Vozel, Vladimir Lukin, & Kacem Chehdi. (2022). Exhaustive Search of Correspondences between Multimodal Remote Sensing Images Using Convolutional Neural Network. Sensors. 22(3). 1231–1231. 6 indexed citations
2.
Uss, Mikhail, Benoît Vozel, Sergey Abramov, & Kacem Chehdi. (2020). Selection of a Similarity Measure Combination for a Wide Range of Multimodal Image Registration Cases. IEEE Transactions on Geoscience and Remote Sensing. 59(1). 60–75. 7 indexed citations
3.
Uss, Mikhail, Benoît Vozel, Vladimir Lukin, & Kacem Chehdi. (2020). Efficient Discrimination and Localization of Multimodal Remote Sensing Images Using CNN-Based Prediction of Localization Uncertainty. Remote Sensing. 12(4). 703–703. 16 indexed citations
4.
Lukin, Vladimir, Sergey Abramov, Mikhail Uss, et al.. (2020). Automation of Processing Multichannel Remote Sensing Images Based on Performance Prediction. HAL (Le Centre pour la Communication Scientifique Directe). 5. 139–144.
5.
Uss, Mikhail, et al.. (2019). Mobile Deployment of NoiseNet: Noise Characteristics Assessment in Real-World Images. l 6. 1112–1117. 1 indexed citations
6.
Abramov, Sergey, Mikhail Uss, Vladimir Lukin, et al.. (2019). Enhancement of Component Images of Multispectral Data by Denoising with Reference. Remote Sensing. 11(6). 611–611. 8 indexed citations
7.
Uss, Mikhail, Benoît Vozel, Vladimir Lukin, & Kacem Chehdi. (2016). Statistical power of intensity- and feature-based similarity measures for registration of multimodal remote sensing images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10004. 1000403–1000403. 3 indexed citations
8.
Zhang, Mo, Benoît Vozel, Kacem Chehdi, et al.. (2016). Accuracy assessment of blind and semi-blind restoration methods for hyperspectral images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10004. 100040P–100040P.
9.
Lukin, Vladimir, et al.. (2015). Efficiency of texture image enhancement by DCT-based filtering. Neurocomputing. 175. 948–965. 19 indexed citations
10.
Uss, Mikhail, Sergey Abramov, Nikolay Ponomarenko, et al.. (2014). Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform. Journal of Applied Remote Sensing. 8(1). 83571–83571. 25 indexed citations
11.
Uss, Mikhail, Benoît Vozel, Vladimir Lukin, & Kacem Chehdi. (2013). Image informative maps for component-wise estimating parameters of signal-dependent noise. Journal of Electronic Imaging. 22(1). 13019–13019. 26 indexed citations
12.
Uss, Mikhail, et al.. (2013). VST-based lossy compression of hyperspectral data for new generation sensors. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8892. 88920L–88920L. 2 indexed citations
13.
Lukin, Vladimir, et al.. (2013). Denoising efficiency for multichannel images corrupted by signal-dependent noise. HAL (Le Centre pour la Communication Scientifique Directe). 99. 340–342. 1 indexed citations
14.
Uss, Mikhail, et al.. (2013). A Precise Lower Bound on Image Subpixel Registration Accuracy. IEEE Transactions on Geoscience and Remote Sensing. 52(6). 3333–3345. 25 indexed citations
15.
Uss, Mikhail. (2012). Maximum likelihood estimation of spatially correlated signal-dependent noise in hyperspectral images. Optical Engineering. 51(11). 111712–111712. 20 indexed citations
16.
Uss, Mikhail, Benoît Vozel, Vladimir Lukin, & Kacem Chehdi. (2011). Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images. IEEE Journal of Selected Topics in Signal Processing. 5(3). 469–486. 69 indexed citations
17.
Uss, Mikhail, et al.. (2011). Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters. EURASIP Journal on Advances in Signal Processing. 2011(1). 25 indexed citations
18.
Uss, Mikhail, Vladimir Lukin, Benoît Vozel, & Kacem Chehdi. (2009). Joint Estimation of Remote Sensing Images and Mixed Noise Parameters. Telecommunications and Radio Engineering. 68(18). 1659–1686. 1 indexed citations
19.
Lukin, Vladimir, Sergey Abramov, Nikolay Ponomarenko, et al.. (2009). Processing of images based on blind evaluation of noise type and characteristics. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7477. 74770B–74770B. 5 indexed citations
20.

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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