Blind Restoration of High-Resolution Ultrasound Video

Chu Chen; Kangning Cui; Pasquale Cascarano; Wei Tang; Elena Loli Piccolomini; Raymond H. Chan

doi:10.1007/978-3-032-04947-6_8

Blind Restoration of High-Resolution Ultrasound Video

Chu Chen^*
, Kangning Cui
, Pasquale Cascarano
, Wei Tang
, Elena Loli Piccolomini
, Raymond H. Chan

^*Corresponding author for this work

Department of Mathematics

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

5 Citations (Scopus)

Abstract

Ultrasound imaging is widely applied in clinical practice, yet ultrasound videos often suffer from low signal-to-noise ratios (SNR) and limited resolutions, posing challenges for diagnosis and analysis. Variations in equipment and acquisition settings can further exacerbate differences in data distribution and noise levels, reducing the generalizability of pre-trained models. This work presents a self-supervised ultrasound video super-resolution algorithm called Deep Ultrasound Prior (DUP). DUP employs a video-adaptive optimization process of a neural network that enhances the resolution of given ultrasound videos without requiring paired training data while simultaneously removing noise. Quantitative and visual evaluations demonstrate that DUP outperforms existing super-resolution algorithms, leading to substantial improvements for downstream applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2025
Subtitle of host publication	28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part III
Editors	James C. Gee, Daniel C. Alexander, Jaesung Hong, Juan Eugenio Iglesias, Carole H. Sudre, Archana Venkataraman, Polina Golland, Jong Hyo Kim, Jinah Park
Place of Publication	Cham
Publisher	Springer
Pages	77-87
Number of pages	11
ISBN (Electronic)	978-3-032-04947-6
ISBN (Print)	978-3-032-04946-9
DOIs	https://doi.org/10.1007/978-3-032-04947-6_8
Publication status	Published - 2026
Event	28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025) - Daejeon Convention Center, Daejeon, Korea, Republic of Duration: 23 Sept 2025 → 27 Sept 2025

Publication series

Name	Lecture Notes in Computer Science
Volume	15962
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025)
Place	Korea, Republic of
City	Daejeon
Period	23/09/25 → 27/09/25

Funding

This work is partially supported by HKRGC (grant number CityU11301120, C1013-21GF, CityU11309922, CityU9380162), ITF (grant number LU BGR 105824, MHP/054/22), and the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government.

Research Keywords

Deep Image Prior
Ejection Fraction
Self-supervised Learning
Ultrasound
Video Super-resolution

RGC Funding Information

RGC-funded

Access Output

10.1007/978-3-032-04947-6_8

Other Access Options

Check CityUHK Library

Permanent Link

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Cite this

Chen, C., Cui, K., Cascarano, P., Tang, W., Piccolomini, E. L., & Chan, R. H. (2026). Blind Restoration of High-Resolution Ultrasound Video. In J. C. Gee, D. C. Alexander, J. Hong, J. E. Iglesias, C. H. Sudre, A. Venkataraman, P. Golland, J. H. Kim, & J. Park (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2025: 28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part III (pp. 77-87). (Lecture Notes in Computer Science; Vol. 15962). Springer . https://doi.org/10.1007/978-3-032-04947-6_8

Chen, Chu ; Cui, Kangning ; Cascarano, Pasquale et al. / Blind Restoration of High-Resolution Ultrasound Video. Medical Image Computing and Computer Assisted Intervention – MICCAI 2025: 28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part III. editor / James C. Gee ; Daniel C. Alexander ; Jaesung Hong ; Juan Eugenio Iglesias ; Carole H. Sudre ; Archana Venkataraman ; Polina Golland ; Jong Hyo Kim ; Jinah Park. Cham : Springer , 2026. pp. 77-87 (Lecture Notes in Computer Science).

@inproceedings{b6c9450c085a4deea2421bbf23730194,

title = "Blind Restoration of High-Resolution Ultrasound Video",

abstract = "Ultrasound imaging is widely applied in clinical practice, yet ultrasound videos often suffer from low signal-to-noise ratios (SNR) and limited resolutions, posing challenges for diagnosis and analysis. Variations in equipment and acquisition settings can further exacerbate differences in data distribution and noise levels, reducing the generalizability of pre-trained models. This work presents a self-supervised ultrasound video super-resolution algorithm called Deep Ultrasound Prior (DUP). DUP employs a video-adaptive optimization process of a neural network that enhances the resolution of given ultrasound videos without requiring paired training data while simultaneously removing noise. Quantitative and visual evaluations demonstrate that DUP outperforms existing super-resolution algorithms, leading to substantial improvements for downstream applications. {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.",

keywords = "Deep Image Prior, Ejection Fraction, Self-supervised Learning, Ultrasound, Video Super-resolution",

author = "Chu Chen and Kangning Cui and Pasquale Cascarano and Wei Tang and Piccolomini, \{Elena Loli\} and Chan, \{Raymond H.\}",

year = "2026",

doi = "10.1007/978-3-032-04947-6\_8",

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isbn = "978-3-032-04946-9",

series = "Lecture Notes in Computer Science",

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pages = "77--87",

editor = "Gee, \{James C.\} and Alexander, \{Daniel C.\} and Jaesung Hong and Iglesias, \{Juan Eugenio\} and Sudre, \{Carole H.\} and Archana Venkataraman and Polina Golland and Kim, \{Jong Hyo\} and Jinah Park",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2025",

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Chen, C , Cui, K, Cascarano, P, Tang, W, Piccolomini, EL & Chan, RH 2026, Blind Restoration of High-Resolution Ultrasound Video. in JC Gee, DC Alexander, J Hong, JE Iglesias, CH Sudre, A Venkataraman, P Golland, JH Kim & J Park (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2025: 28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part III. Lecture Notes in Computer Science, vol. 15962, Springer , Cham, pp. 77-87, 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025), Daejeon, Korea, Republic of, 23/09/25. https://doi.org/10.1007/978-3-032-04947-6_8

Blind Restoration of High-Resolution Ultrasound Video. / Chen, Chu ; Cui, Kangning; Cascarano, Pasquale et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2025: 28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part III. ed. / James C. Gee; Daniel C. Alexander; Jaesung Hong; Juan Eugenio Iglesias; Carole H. Sudre; Archana Venkataraman; Polina Golland; Jong Hyo Kim; Jinah Park. Cham: Springer , 2026. p. 77-87 (Lecture Notes in Computer Science; Vol. 15962).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Blind Restoration of High-Resolution Ultrasound Video

AU - Chen, Chu

AU - Cui, Kangning

AU - Cascarano, Pasquale

AU - Tang, Wei

AU - Piccolomini, Elena Loli

AU - Chan, Raymond H.

PY - 2026

Y1 - 2026

N2 - Ultrasound imaging is widely applied in clinical practice, yet ultrasound videos often suffer from low signal-to-noise ratios (SNR) and limited resolutions, posing challenges for diagnosis and analysis. Variations in equipment and acquisition settings can further exacerbate differences in data distribution and noise levels, reducing the generalizability of pre-trained models. This work presents a self-supervised ultrasound video super-resolution algorithm called Deep Ultrasound Prior (DUP). DUP employs a video-adaptive optimization process of a neural network that enhances the resolution of given ultrasound videos without requiring paired training data while simultaneously removing noise. Quantitative and visual evaluations demonstrate that DUP outperforms existing super-resolution algorithms, leading to substantial improvements for downstream applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

AB - Ultrasound imaging is widely applied in clinical practice, yet ultrasound videos often suffer from low signal-to-noise ratios (SNR) and limited resolutions, posing challenges for diagnosis and analysis. Variations in equipment and acquisition settings can further exacerbate differences in data distribution and noise levels, reducing the generalizability of pre-trained models. This work presents a self-supervised ultrasound video super-resolution algorithm called Deep Ultrasound Prior (DUP). DUP employs a video-adaptive optimization process of a neural network that enhances the resolution of given ultrasound videos without requiring paired training data while simultaneously removing noise. Quantitative and visual evaluations demonstrate that DUP outperforms existing super-resolution algorithms, leading to substantial improvements for downstream applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

KW - Deep Image Prior

KW - Ejection Fraction

KW - Self-supervised Learning

KW - Ultrasound

KW - Video Super-resolution

UR - https://www.scopus.com/pages/publications/105017851370

UR - https://www.scopus.com/pages/publications/105017851370#tab=impact

U2 - 10.1007/978-3-032-04947-6_8

DO - 10.1007/978-3-032-04947-6_8

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-032-04946-9

T3 - Lecture Notes in Computer Science

SP - 77

EP - 87

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2025

A2 - Gee, James C.

A2 - Alexander, Daniel C.

A2 - Hong, Jaesung

A2 - Iglesias, Juan Eugenio

A2 - Sudre, Carole H.

A2 - Venkataraman, Archana

A2 - Golland, Polina

A2 - Kim, Jong Hyo

A2 - Park, Jinah

PB - Springer

CY - Cham

T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025)

Y2 - 23 September 2025 through 27 September 2025

ER -

Chen C , Cui K, Cascarano P, Tang W, Piccolomini EL, Chan RH. Blind Restoration of High-Resolution Ultrasound Video. In Gee JC, Alexander DC, Hong J, Iglesias JE, Sudre CH, Venkataraman A, Golland P, Kim JH, Park J, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2025: 28th International Conference, Daejeon, South Korea, September 23–27, 2025, Proceedings, Part III. Cham: Springer . 2026. p. 77-87. (Lecture Notes in Computer Science). Epub 2025 Sept 21. doi: 10.1007/978-3-032-04947-6_8

Blind Restoration of High-Resolution Ultrasound Video

Abstract

Publication series

Conference

Funding

Research Keywords

RGC Funding Information

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Other Access Options

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ITF: Artificial Intelligence Methods and Applications for Breast Cancer Diagnosis and Treatment in Clinical Trials

GRF: Efficient Mathematical and Data-driven Models for Closed-loop Adaptive Optics Systems for Ground-based Astronomy

GRF: Novel Computational Methods for Three-dimensional Point Source Localization Based on Point Spread Function Analytics

Cite this

Blind Restoration of High-Resolution Ultrasound Video

Abstract

Publication series

Conference

Funding

Research Keywords

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

ITF: Artificial Intelligence Methods and Applications for Breast Cancer Diagnosis and Treatment in Clinical Trials

GRF: Efficient Mathematical and Data-driven Models for Closed-loop Adaptive Optics Systems for Ground-based Astronomy

GRF: Novel Computational Methods for Three-dimensional Point Source Localization Based on Point Spread Function Analytics

Cite this