Total Variation Deconvolution-Based Enhancement of Spatial Resolution in Microwave Radiometer Measurements

Zhen Tan, Weidong Hu*, Zhihao Xu, Kaiqi Zhang, Shi Qiao, Linhai Jia

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Microwave Imager has emerged as a pivotal payload within meteorological monitoring and assimilation systems, attributed to its comprehensive observational capabilities, which span all weather conditions, daily cycles, and extensive spatial ranges. Nonetheless, the resolution of the channel data from the Microwave Imager is constrained due to limitations inherent in the satellite payload design process. Consequently, this research introduces a remote sensing image restoration algorithm, leveraging Total Variation (TV) regularization deconvolution. Experimental outcomes, derived from both simulated and actual data, reveal that the method offers enhanced image restoration and potent noise suppression. The Peak Signal-to-Noise Ratio (PSNR) of the restored image attains a value of 38.7, while the Structural Similarity (SSIM) achieves a measurement of 0.97.

Original languageEnglish
Title of host publicationProceedings - 2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350358971
DOIs
Publication statusPublished - 2023
Event2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023 - Guilin, China
Duration: 10 Nov 202313 Nov 2023

Publication series

NameProceedings - 2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023

Conference

Conference2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023
Country/TerritoryChina
CityGuilin
Period10/11/2313/11/23

Keywords

  • image deconvolution
  • microwave imager
  • resolution enhancement

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