Cited by 403Image Enhancement TechniquesImage and Signal Denoising MethodsAdvanced Image Processing Techniques

Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal

Buyue Zhang, Allebach, J.P.·2008·IEEE Transactions on Image Processing

Abstract

In this paper, we present the adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. The ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. It is an approach to sharpness enhancement that is fundamentally different from the unsharp mask (USM). This new approach to slope restoration also differs significantly from previous slope restoration algorithms in that the ABF does not involve detection of edges or their orientation, or extraction of edge profiles. In the ABF, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The ABF is able to smooth the noise, while enhancing edges and textures in the image. The parameters of the ABF are optimized with a training procedure. ABF restored images are significantly sharper than those restored by the bilateral filter. Compared with an USM based sharpening method-the optimal unsharp mask (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without the halo artifacts that appear in the OUM restored image. In terms of noise removal, ABF also outperforms the bilateral filter and the OUM. We demonstrate that ABF works well for both natural images and text images.

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Crossref · 10.1109/tip.2008.919949

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  • Positive results have been shown for bilateral filtering and Gaussian blur algorithms among image-processing algorithms.

    “Of the image-processing algorithms, positive results have been shown for bilateral filtering and Gaussian blur algorithms [49,50].”

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Authors
Zhang, B., & Allebach, J. P.
Affiliations
  • Texas Instruments (United States) (US)
  • Purdue University West Lafayette (US)
Venue
IEEE Transactions on Image Processing
Published
2008-05
Volume
17
Issue
5
Pages
664-678
ISSN
  • 1057-7149 (ISSN-L)
  • 1941-0042
OpenAlex
W2145367339
Cited by
403
References
54
Citations by year
2026: 11 · 2025: 16 · 2024: 16 · 2023: 36 · 2022: 20 · 2021: 25 · 2020: 19 · 2019: 27 · 2018: 31 · 2017: 26 · 2016: 24 · 2015: 29 · 2014: 31 · 2013: 24 · 2012: 27
FWCI
19.66
Type
journal article
Language
en
Open access
closed
Locations
  • IEEE Transactions on Image Processing (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
Keywords
adaptive bilateral filter, image sharpening, noise reduction, histogram transformation, unsharp masking, halo artifacts, edge enhancement, texture enhancement, text images
MeSH terms
Algorithms, Artificial Intelligence, Computer Graphics, Image Enhancement, Image Interpretation, Computer-Assisted, Numerical Analysis, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Reproducibility of Results, Information Storage and Retrieval, Artifacts

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