A Review on Speckle Noise Reduction Techniques
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paper_title: A hybrid filter for image despeckling with wavelet-based denoising and spatial filtering

paper_content:

Noise is one of the most common problems found in different imaging applications. Even a high resolution photo is bound to have some noise in it. Image denoising algorithms may be the oldest in image processing. Many methods, regardless of their implementation, share the same basic idea: noise reduction through image blurring. However, a universal “best” approach has yet to be found. Wavelet transform has been studied extensively in recent years and it has been widely used for noise reduction. In this paper, we propose a novel technique for image despeckling that combines wavelet denoising and an enhanced adaptive version of the old Kuan filter, which results in a significant gain with respect to speckle noise filters and the simple wavelet denoising technique.
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Title: A Review on Speckle Noise Reduction Techniques

Section 1: Introduction
Description 1: This section introduces the problem of noise in imaging systems, particularly focusing on speckle noise encountered in medical imaging. 

Section 2: Statistical Model of Speckle in Ultrasound
Description 2: In this section, discuss the statistical models used to understand and represent speckle noise in ultrasound imaging.

Section 3: Speckle Filtering Methods
Description 3: This section outlines various methods for speckle noise reduction, divided into spatial domain techniques and transform domain techniques.

Section 3.1: Spatial Domain Filtering
Description 3.1: Discuss the spatial domain techniques for filtering speckle noise, including explanations of common filters.

Section 3.1.1: Box Filter
Description 3.1.1: Describe the box filter method, its advantages, and its disadvantages.

Section 3.1.2: Median Filter
Description 3.1.2: Explain the median filter technique, focusing on its effectiveness against different types of noise and its computational requirements.

Section 3.1.3: Statistic Lee Filter
Description 3.1.3: Detail the Lee filter, its working principles, and any limitations.

Section 3.1.4: Statistic Kuan Filter
Description 3.1.4: Discuss the Kuan filter, its approach compared to the Lee filter, and its limitations.

Section 3.1.5: Frost Filter
Description 3.1.5: Describe the Frost filter and how it adapts to different variances within an image for effective noise reduction.

Section 3.1.6: Srad Filter
Description 3.1.6: Explore the SRAD filter technique, which uses non-linear anisotropic diffusion for reducing speckle noise.

Section 3.2: Transform Domain Techniques
Description 3.2: Explain the methods that work in the transform domain to reduce speckle noise, including their principles and execution.

Section 3.2.1: Fourier Transform
Description 3.2.1: Describe the Fourier transform approach for speckle noise reduction, including its steps and applications.

Section 3.2.2: Wavelet Transform
Description 3.2.2: Discuss the wavelet transform method, its benefits in multi-resolution analysis, and its applications in noise reduction.

Section 3.2.3: Wavelet Domain Noise Filtering
Description 3.2.3: Explain how noise filtering is achieved in the wavelet domain and the specific processes used.

Section 4: Thresholding
Description 4: Discuss the role of thresholding techniques in speckle noise reduction, including soft and hard thresholding methods.

Section 4.1: Soft Thresholding
Description 4.1: Explain the soft thresholding technique and how it improves image quality by reducing discontinuities.

Section 4.2: Hard Thresholding
Description 4.2: Describe the hard thresholding technique and its application in detail coefficient handling.

Section 5: Conclusion
Description 5: Summarize the findings and discussion on the various filtering methods, highlighting the advantages of wavelet transform techniques over traditional methods.