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RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

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Correction: Tu et al. Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography. <i>Sensors</i> 2024, <i>24</i>, 3097.

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Decomposed Multilateral Filtering for Accelerating Filtering with Multiple Guidance Images.

Haruki Nogami1, Yamato Kanetaka1, Yuki Naganawa1

  • 1Department of Computer Science, Faculty of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan.

Sensors (Basel, Switzerland)
|January 26, 2024
PubMed
Summary

This study introduces an efficient algorithm for edge-preserving filtering using multiple guidance images, enhancing sensor fusion applications. The new method, Decompose Multilateral Filtering (DMF), speeds up processing while effectively utilizing multiple data sources.

Keywords:
constant-time filteringedge-preserving filteringmultilateral filtering

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Multimodal signal processing and sensor fusion are critical in modern image sensing.
  • Edge-preserving filtering is vital for applications like scene property estimation and inverse rendering.
  • Existing accelerated filters struggle to effectively utilize multiple guidance images.

Purpose of the Study:

  • To develop an efficient edge-preserving filtering algorithm capable of handling multiple guidance images.
  • To extend existing efficient filtering methods to support multilateral filtering.
  • To address the computational time limitations of traditional edge-preserving filters.

Main Methods:

  • Proposed an algorithm named Decompose Multilateral Filtering (DMF).
  • DMF decomposes the filtering process into a series of constant-time operations.
  • Extends efficient edge-preserving filters to incorporate multiple guidance images.

Main Results:

  • The Decompose Multilateral Filtering (DMF) algorithm demonstrates high efficiency.
  • The method effectively utilizes multiple guidance images for improved filtering.
  • Experimental results confirm the algorithm's suitability for diverse applications.

Conclusions:

  • The proposed Decompose Multilateral Filtering (DMF) algorithm offers an efficient solution for edge-preserving filtering with multiple guidance images.
  • This advancement is beneficial for various sensor fusion applications requiring enhanced image processing.
  • The algorithm successfully overcomes the limitations of previous methods in handling multiple guidance data.