LOW SNR MULTIFRAME REGISTRATION FOR CUBESATS

Evan Widloski, Farzad Kamalabadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a registration algorithm which jointly estimates motion and the ground truth image from a set of noisy frames under rigid, constant translation. The algorithm is non-iterative and needs no hyperparameter tuning. It requires a fixed number of FFT, multiplication, and downsampling operations for a given input size, enabling fast implementation on embedded platforms like cubesats where on-board image fusion can greatly save on limited downlink bandwidth. The algorithm is optimal in the maximum likelihood sense for additive white Gaussian noise and non-stationary Gaussian approximations of Poisson noise. Accurate registration is achieved for very low SNR, even when visible features are below the noise floor.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PublisherIEEE Computer Society
Pages3773-3777
Number of pages5
ISBN (Electronic)9781665496209
DOIs
StatePublished - 2022
Externally publishedYes
Event29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
Duration: Oct 16 2022Oct 19 2022

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
Country/TerritoryFrance
CityBordeaux
Period10/16/2210/19/22

Keywords

  • embedded signal processing
  • image registration
  • low SNR
  • maximum likelihood
  • motion estimation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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