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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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Related Experiment Video

Updated: May 23, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Recursive algorithms for implementing digital image filters.

L A Ferrari1, P V Sankar, S Shinnaka

  • 1Department of Radiological Sciences and Electrical Engineering, University of California, Irvine, CA 92717.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 21, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces efficient recursive algorithms using B-spline functions for digital image filtering, offering faster hardware implementations than traditional methods. These B-spline image filters provide a significant advancement in image processing efficiency.

Related Experiment Videos

Last Updated: May 23, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

  • Digital Image Processing
  • Computer Vision
  • Applied Mathematics

Background:

  • Digital image filters are crucial for image processing tasks.
  • Existing methods like direct spatial domain filtering and Fast Fourier Transform (FFT) have computational limitations.
  • Spatially varying filters are desirable for advanced image manipulation.

Purpose of the Study:

  • To develop efficient recursive algorithms for two-dimensional linear digital image filters.
  • To explore the use of B-spline functions for representing point spread functions in image filters.
  • To propose a novel computer architecture for implementing these B-spline filters.

Main Methods:

  • Utilizing B-spline functions to represent the point spread function (PSF) of digital image filters.
  • Developing recursive algorithms based on B-spline representations.
  • Applying the Z-transform to derive a discrete version of Duhamel's theorem for filter implementation.
  • Proposing a dedicated computer architecture for B-spline image filters.

Main Results:

  • The developed B-spline based recursive algorithms offer more efficient hardware implementations compared to direct spatial domain filters.
  • FFT-based implementations are also outperformed in terms of efficiency by the proposed B-spline approach.
  • A detailed complexity analysis and comparison with existing methods are provided, highlighting the advantages of the B-spline filters.

Conclusions:

  • B-spline functions provide an effective basis for creating efficient recursive algorithms for 2D linear digital image filters.
  • The proposed B-spline image filters and associated architecture present a computationally advantageous alternative for image processing applications.
  • This approach enables more efficient spatial domain filtering, including for spatially varying filters.