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A discriminative approach for wavelet denoising.

Y Hel-Or¹, D Shaked

¹Efi Arazi School of Computer Science, Herzliya, Israel. toky@idc.ac.il

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

|April 9, 2008

Summary

This study introduces a novel discriminative wavelet denoising method using learned mapping functions (MFs) for noise-free image generation. This approach achieves state-of-the-art performance without needing image priors, offering versatile applications in image restoration.

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Area of Science:

Image processing and computer vision
Signal processing
Machine learning for image restoration

Background:

Traditional wavelet denoising often relies on descriptive approaches requiring image or noise prior models.
Existing methods like soft/hard thresholding have limitations, especially in over-complete representations.

Purpose of the Study:

To propose a discriminative approach for wavelet denoising using learned mapping functions (MFs).
To develop a method that bypasses the need for explicit image or noise prior modeling.
To demonstrate the adaptability of the framework for various image restoration tasks.

Main Methods:

A discriminative learning framework is employed to derive MFs directly from example image data.
Least-squares fitting is utilized to train the mapping functions on an ensemble of images.
The learned MFs are applied to wavelet transform coefficients for noise reduction.

Main Results:

The developed MFs are fundamentally different from traditional thresholding techniques in over-complete settings.
The proposed method achieves performance comparable to current state-of-the-art image denoising techniques.
The framework demonstrates successful application to diverse restoration problems beyond simple denoising.

Conclusions:

The discriminative wavelet denoising approach offers a powerful alternative to traditional methods.
The learned mapping functions provide a flexible and effective tool for image restoration.
This framework expands the applicability of shrinkage-based techniques to new challenges like deblurring and artifact removal.