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Deconvolution01:20

Deconvolution

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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.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
764

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Image denoising using nonsubsampled shearlet transform and twin support vector machines.

Hong-Ying Yang1, Xiang-Yang Wang2, Pan-Pan Niu1

  • 1School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 8, 2014
PubMed
Summary

This study introduces a novel image denoising method using Nonsubsampled Shearlet Transform (NSST) and Twin Support Vector Machines (TSVMs). The technique effectively preserves image edges and textures while removing noise, outperforming existing methods.

Keywords:
Adaptive denoising thresholdImage denoisingNonsubsampled shearlet transformTwin support vector machines

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Area of Science:

  • Image Processing
  • Computer Vision
  • Machine Learning

Background:

  • Image denoising is crucial but challenging, especially for preserving edges and textures.
  • Nonsubsampled Shearlet Transform (NSST) offers effective multi-scale and multi-direction analysis for image decomposition.
  • Existing denoising methods struggle with simultaneous noise removal and feature preservation.

Purpose of the Study:

  • To propose a new edge and texture-preserving image denoising scheme.
  • To leverage the strengths of NSST and Twin Support Vector Machines (TSVMs) for improved denoising.
  • To develop an adaptive thresholding approach for denoising NSST coefficients.

Main Methods:

  • Image decomposition into frequency and orientation subbands using NSST.
  • Feature vector creation based on spatial geometric regularity in the NSST domain.
  • Training a TSVMs model to classify NSST coefficients as information- or noise-related.
  • Applying adaptive thresholding to denoise detail subbands.

Main Results:

  • The proposed method demonstrates superior performance in subjective and objective evaluations compared to state-of-the-art techniques.
  • Experimental results show excellent preservation of edges and textures.
  • Effective noise removal is achieved across various image types.

Conclusions:

  • The NSST-based denoising method utilizing TSVMs is highly effective for edge and texture preservation.
  • This approach offers a significant advancement in image denoising technology.
  • The method provides a robust solution for challenging image processing tasks.