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A steerable complex wavelet construction and its application to image denoising.

Anil Anthony Bharath¹, Jeffrey Ng

¹Department of Bioengineering, Imperial College, London SW7 2AZ, UK.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 21, 2005

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This study introduces a new complex steerable wavelet for image denoising. The method extracts features to identify edges and corners, offering a promising approach for structure-based noise reduction.

Area of Science:

Image Processing
Wavelet Theory
Computer Vision

Background:

Traditional image denoising methods struggle with preserving structural details.
Wavelet transforms offer multi-resolution analysis but require effective feature extraction for targeted denoising.

Purpose of the Study:

To design a novel complex steerable wavelet.
To develop transform-space features for corner and edge detection.
To apply these features for structure-based image denoising.

Main Methods:

Construction of complex steerable wavelets with novel radial characteristics and interpolation functions.
Development of bandpass filters with symmetry and antisymmetry about a steerable axis.
Engineering filters into a multirate system for analysis and synthesis subband filtering.

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Main Results:

Generated transform-space features indicating corner and edge presence and orientation.
Applied features directly to image denoising.
Achieved denoising performance comparable to wavelet coring with global thresholds and an Oracle shrinkage technique.

Conclusions:

The proposed complex steerable wavelet offers a promising avenue for structure-based denoising.
The method effectively utilizes extracted structural features for noise reduction.
Further development may lead to state-of-the-art denoising performance.