Bhabesh Deka

606 citations
62 papers · 350 · h-index 12

Impact in

Papers in

Bhabesh Deka

54 papers receiving 332 citations

Peers

Bhabesh Deka
Comparison fields: 5 of 73
  • Media Technology 70
  • Computer Vision and Pattern Recognition 120
  • Computational Mechanics 77
  • Radiology, Nuclear Medicine and Imaging 68
  • Signal Processing 27
Replace Stephan Didas with:
Stephan Didas Germany
Jingfei He China
Gyanendra Sheoran India
Olivier Laligant France
Satish Mulleti India
Ramsin Khoshabeh United States
Toru Kurihara Japan
Hans‐Georg Stark Germany
Niels Jørgen Christensen Denmark
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Bhabesh Deka relative to Stephan Didas Germany Stephan Didas's profile →
Citations per field
00.5×4.6×
Stephan Didas · 1×
Citations per year

Countries citing papers authored by Bhabesh Deka

Since Specialization
Citations

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

Fields of papers citing papers by Bhabesh Deka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Bhabesh Deka, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bhabesh Deka Line = papers co-authored together Bhabesh Deka links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 62 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201525
2 202218
3 202115
4 201315
5 202115
6 202012
7 202012
8 201812
9
A Multiscale Detection based Adaptive Median Filter for the Removal of Salt and Pepper Noise from Highly Corrupted Images
201311
10 202111
11 201711
12 202111
13 202210
14 201010
15 201610
16 20249
17 20189
18 20238
19 20118
20 20228

About Bhabesh Deka

Bhabesh Deka is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Biomedical Engineering, Media Technology and Radiology, Nuclear Medicine and Imaging, having authored 62 papers that have together received 350 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (23 papers), Sparse and Compressive Sensing Techniques (20 papers), Advanced Image Processing Techniques (19 papers), Advanced Image Fusion Techniques (16 papers), Photoacoustic and Ultrasonic Imaging (11 papers), Advanced MRI Techniques and Applications (10 papers), Blind Source Separation Techniques (8 papers) and Heart Rate Variability and Autonomic Control (5 papers). The work is most often cited by research in Media Technology (70 citations), Computer Vision and Pattern Recognition (120 citations), Computational Mechanics (77 citations), Radiology, Nuclear Medicine and Imaging (68 citations) and Signal Processing (27 citations). Bhabesh Deka has collaborated with scholars based in India, United Kingdom and Germany. Frequent co-authors include Sumit Datta, Prabin Kumar Bora, Vincent Fusco, Rahul Sharma, Okan Yurduseven, A. Srinivasan, Debashis Ghosh, Y. K. Takahashi, K. Hono and Ranjan K. Singh. Their work appears in journals such as Biomedical Signal Processing and Control, IEEE Access, IEEE Sensors Journal, IEEE Transactions on Artificial Intelligence and IEEE Signal Processing Letters.

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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