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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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A Novel Approach to Overcome Movement Artifact When Using a Laser Speckle Contrast Imaging System for Alternating Speeds of Blood Microcirculation

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Alternating minimization algorithm for speckle reduction with a shifting technique.

Hyenkyun Woo¹, Sangwoon Yun

¹Department of Mathematical Sciences, Seoul National University, Seoul, Korea. hyenkyun@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 23, 2011

Summary

This study introduces an efficient method for reducing speckle noise in synthetic aperture radar (SAR) images. The novel approach simplifies calculations, making it faster for large-scale SAR image processing.

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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

Remote Sensing
Image Processing
Computational Mathematics

Background:

Speckle noise in Synthetic Aperture Radar (SAR) images degrades interpretability.
Total Variation (TV) regularization is effective for speckle reduction due to its edge-preserving properties.
Existing algorithms for TV-regularized SAR speckle reduction often involve computationally intensive inner iterations or Laplacian operator inversions.

Purpose of the Study:

To develop a computationally efficient and highly parallelizable algorithm for SAR speckle reduction.
To overcome the limitations of existing methods that require inner iterations or Laplacian inversions.
To improve the speed of processing large-sized SAR images.

Main Methods:

Adaptation of Tseng's alternating minimization algorithm.
Incorporation of a shifting technique to avoid complex computations.
Implementation of a method that eliminates the need for inner iterations or Laplacian operator inversions.

Main Results:

The proposed method successfully reduces speckle noise in SAR images.
The algorithm demonstrates high efficiency and parallelizability, suitable for large datasets.
Numerical results indicate superior performance compared to state-of-the-art methods in terms of processing time (CPU time).

Conclusions:

The adapted Tseng's algorithm with a shifting technique offers a simple and efficient solution for SAR speckle reduction.
This method significantly reduces computational burden, making it ideal for processing extensive SAR imagery.
The approach represents a notable advancement in efficient speckle removal for SAR applications.