Adaptive weighted highpass filters using multiscale analysis

Robert D. Nowak; Richard G. Baraniuk

doi:10.1109/83.701168

Adaptive weighted highpass filters using multiscale analysis

Robert D. Nowak, Richard G. Baraniuk

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

In this correspondence, we propose a general framework for studying a class of weighted highpass filters. Our framework, based on a multiscale signal decomposition, allows us to study a wide class of filters and to assess the merits of each. We derive an automatic procedure to tune a filter to the local structure of the image under consideration. The entire algorithm is fully automatic and requires no parameter specification from the user. Several simulations demonstrate the efficacy of the proposed algorithm.

Original language	English (US)
Pages (from-to)	1068-1074
Number of pages	7
Journal	IEEE Transactions on Image Processing
Volume	7
Issue number	7
DOIs	https://doi.org/10.1109/83.701168
State	Published - 1998

Keywords

Enhancement
Nonlinear filtering
Wavelets

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Graphics and Computer-Aided Design
Software
Theoretical Computer Science
Computational Theory and Mathematics
Computer Vision and Pattern Recognition

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10.1109/83.701168

Cite this

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title = "Adaptive weighted highpass filters using multiscale analysis",

abstract = "In this correspondence, we propose a general framework for studying a class of weighted highpass filters. Our framework, based on a multiscale signal decomposition, allows us to study a wide class of filters and to assess the merits of each. We derive an automatic procedure to tune a filter to the local structure of the image under consideration. The entire algorithm is fully automatic and requires no parameter specification from the user. Several simulations demonstrate the efficacy of the proposed algorithm.",

keywords = "Enhancement, Nonlinear filtering, Wavelets",

author = "Nowak, {Robert D.} and Baraniuk, {Richard G.}",

note = "Funding Information: Manuscript received February 24, 1996; revised August 26, 1997. This work was supported by the National Science Foundation under Grants MIP-9701692 and MIP-9457438, and by the Office of Naval Research under Grant N00014-95-1-084. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Henri Maitre. Copyright: Copyright 2011 Elsevier B.V., All rights reserved.",

year = "1998",

doi = "10.1109/83.701168",

language = "English (US)",

volume = "7",

pages = "1068--1074",

journal = "IEEE Transactions on Image Processing",

issn = "1057-7149",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "7",

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TY - JOUR

T1 - Adaptive weighted highpass filters using multiscale analysis

AU - Nowak, Robert D.

AU - Baraniuk, Richard G.

N1 - Funding Information: Manuscript received February 24, 1996; revised August 26, 1997. This work was supported by the National Science Foundation under Grants MIP-9701692 and MIP-9457438, and by the Office of Naval Research under Grant N00014-95-1-084. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Henri Maitre. Copyright: Copyright 2011 Elsevier B.V., All rights reserved.

PY - 1998

Y1 - 1998

N2 - In this correspondence, we propose a general framework for studying a class of weighted highpass filters. Our framework, based on a multiscale signal decomposition, allows us to study a wide class of filters and to assess the merits of each. We derive an automatic procedure to tune a filter to the local structure of the image under consideration. The entire algorithm is fully automatic and requires no parameter specification from the user. Several simulations demonstrate the efficacy of the proposed algorithm.

AB - In this correspondence, we propose a general framework for studying a class of weighted highpass filters. Our framework, based on a multiscale signal decomposition, allows us to study a wide class of filters and to assess the merits of each. We derive an automatic procedure to tune a filter to the local structure of the image under consideration. The entire algorithm is fully automatic and requires no parameter specification from the user. Several simulations demonstrate the efficacy of the proposed algorithm.

KW - Enhancement

KW - Nonlinear filtering

KW - Wavelets

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JF - IEEE Transactions on Image Processing

SN - 1057-7149

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ER -

Adaptive weighted highpass filters using multiscale analysis

Abstract

Keywords

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