Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
19851.1k citationsD. Kuan, Alexander A. Sawchuk et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Adaptive restoration of images with speckle
1987534 citationsD. Kuan, Alexander A. Sawchuk et al.IEEE Transactions on Acoustics Speech and Signal Processingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of D. Kuan'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 D. Kuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. Kuan more than expected).
This network shows the impact of papers produced by D. Kuan. 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 D. Kuan. The network helps show where D. Kuan may publish in the future.
Co-authorship network of co-authors of D. Kuan
This figure shows the co-authorship network connecting the top 25 collaborators of D. Kuan.
A scholar is included among the top collaborators of D. Kuan 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 D. Kuan. D. Kuan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kuan, D., et al.. (1988). Autonomous robotic vehicle road following. IEEE Transactions on Pattern Analysis and Machine Intelligence. 10(5). 648–658.67 indexed citations
11.
Kuan, D., Alexander A. Sawchuk, T. Strand, & Pierre Chavel. (1987). Adaptive restoration of images with speckle. IEEE Transactions on Acoustics Speech and Signal Processing. 35(3). 373–383.534 indexed citations breakdown →
Kuan, D., et al.. (1986). A real-time road following and road junction detection vision system for autonomous vehicles. 1127–1132.9 indexed citations
14.
Kuan, D., et al.. (1985). Mission Planning System for an Autonomous Vehicle.. 162–167.8 indexed citations
15.
Kuan, D., Alexander A. Sawchuk, Timothy C. Strand, & Pierre Chavel. (1985). Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-7(2). 165–177.1115 indexed citations breakdown →
16.
Kuan, D., et al.. (1983). Intelligent interpretation of 3-D imagery. AIDS.2 indexed citations
17.
Kuan, D., et al.. (1983). Model-based interpretation of range imagery. National Conference on Artificial Intelligence. 210–215.15 indexed citations
18.
Kuan, D., Alexander A. Sawchuk, T. C. Strand, & Pierre Chavel. (1983). <title>Adaptive Restoration Of Images With Speckle</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 359. 28–38.6 indexed citations
19.
Kuan, D., Alexander A. Sawchuk, T. C. Strand, & Pierre Chavel. (1981). Discrete speckle image modeling and restoration (A). Journal of the Optical Society of America A. 71. 1585.2 indexed citations
20.
Kuan, D., et al.. (1981). Nonstationary two-dimensional recursive image restoration (A). Journal of the Optical Society of America A. 71. 1641.3 indexed citations
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.