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Related Concept Videos

Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Super-resolution Fluorescence Microscopy01:37

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Related Experiment Video

Updated: May 21, 2026

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
10:03

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy

Published on: June 27, 2014

Image denoising using the higher order singular value decomposition.

Ajit Rajwade1, Anand Rangarajan, Arunava Banerjee

  • 1DA-IICT, Post Bag No. 4, Near Indroda Circle, Gandhinagar 382007, Gujarat, India. avr@cise.ufl.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 27, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a simple machine learning method for image denoising using Higher Order Singular Value Decomposition (HOSVD). The technique effectively removes noise from grayscale and color images, achieving state-of-the-art results for color image denoising.

Related Experiment Videos

Last Updated: May 21, 2026

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
10:03

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy

Published on: June 27, 2014

Area of Science:

  • Computer Vision
  • Machine Learning
  • Signal Processing

Background:

  • Image noise significantly degrades visual quality and hinders downstream analysis.
  • Existing denoising methods often struggle with complex noise patterns or introduce artifacts.
  • Higher Order Singular Value Decomposition (HOSVD) offers a promising framework for multi-dimensional data analysis.

Purpose of the Study:

  • To develop a simple yet effective patch-based machine learning technique for image denoising.
  • To leverage HOSVD for noise reduction in both grayscale and color images.
  • To establish a principled approach for parameter selection and noise estimation.

Main Methods:

  • A patch-based approach grouping similar image patches into 3D stacks.
  • Application of Higher Order Singular Value Decomposition (HOSVD) to these stacks.
  • Manipulation of HOSVD coefficients via hard thresholding, followed by inverse transform.

Main Results:

  • Demonstrated excellent denoising performance on grayscale and color images.
  • Achieved state-of-the-art results for color image denoising, outperforming existing algorithms.
  • Presented a criterion for optimal patch-size selection and noise variance estimation.

Conclusions:

  • The proposed HOSVD-based technique offers an elegant and effective solution for image denoising.
  • The method demonstrates superior performance, particularly on color images, at various noise levels.
  • The principled parameter selection and noise estimation criteria enhance the robustness and applicability of the technique.