A Review Paper: Noise Models in Digital Image Processing
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paper_title: Handbook Of Image And Video Processing

paper_content:

Thank you for reading handbook of image and video processing. As you may know, people have look numerous times for their favorite novels like this handbook of image and video processing, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their computer. handbook of image and video processing is available in our book collection an online access to it is set as public so you can get it instantly. Our books collection hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the handbook of image and video processing is universally compatible with any devices to read.
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paper_title: A Comparative Study of Image Denoising Techniques

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Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This paper reviews the Noise models, Noise types and classification of image denoising techniques. This paper presents a comparative analysis of various noise suppression algorithms.
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paper_title: Image denoising using wavelet transform and median filtering

paper_content:

During acquisition of an image, from its source, noise becomes integral part of it, which is very difficult to remove. Various algorithms have been used in past to denoise images. Image denoising still has scope for improvement. In this paper we present a new image denoising algorithm based on combined effect of wavelet transform and median filtering. The algorithm removes most of the noisy part from the image and maintains the quality. The level of wavelet decomposition is restricted to three. The renowned index Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) demonstrate marked improvement of image denoising over other methods.
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paper_title: Fundamentals of nonlinear digital filtering

paper_content:

Nonlinear Signal Processing Signal Processing Model Signal and Noise Models Fundamental Problems in Noise Removal Algorithms Statistical Preliminaries Random Variables and Distributions Signal and Noise Models Estimation Some Useful Distributions 1001 Solutions Trimmed Mean Filters Other Trimmed Mean Filters L-Filters C-Filters (Ll-Filters) Weighted Median Filters Ranked-Order and Weighted Order Statistic Filters Multistage Median Filters Median Hybrid Filters Edge-Enhancing Selective Filters Rank Selection Filters M-Filters R-Filters Weighted Majority with Minimum Range Filters Nonlinear Mean Filters Stack Filters Generalizations of Stack Filters Morphological Filters Soft Morphological Filters Polynomial Filters Data-Dependent Filters Decision-Based Filters Iterative, Cascaded, and Recursive Filters Some Numerical Measures of Nonlinear Filters Discussion Statistical Analysis and Optimization of Nonlinear Filters Methods Based on Order Statistics Stack Filters Multistage and Hybrid Filters Discussion Exercises Bibliography Index
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paper_title: BROWNIAN MOTION-PAST AND PRESENT

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We discuss Brownian motion from the more elementary viewpoint presented by Einstein in 1908. Later developments and applications are briefly reviewed. One of the celebrated papers of 1905 deals with a quantitative study of the random motion.A couple of years later Einstein wrote a simplified account of the seminal work and we will present this version in this article. A direct test of the molecular kinetic theory was his obsession during his student days.The analysis of motion of suspended particles in a liquid was an attempt to make observable predictions that when tested would establish,beyond doubt, the correctness of the molecular point of view. The analysis begins by considering a non-uniform solution in a container
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paper_title: Improving the quantification of Brownian motion

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Brownian motion experiments have become a staple of the undergraduate advanced laboratory, yet quantification of these experiments is difficult, typically producing errors of 10%–15% or more. Here, we discuss the individual sources of error in the experiment: sampling error, uncertainty in the diffusion coefficient, tracking error, vibration, and microscope drift. We model each source of error using theoretical and computational methods and compare the model to our experimental data. Finally, we describe various ways to reduce each source of error to less than 1%, improving the quantification of Brownian motion.
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paper_title: Inexpensive Microscopy for Introductory Laboratory Courses

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We present an inexpensive apparatus for bright field and fluorescence microscopy with video capture, suitable for introductory laboratory courses. Experiments on Brownian motion and the Boltzmann distribution of suspended particles in a gravitational field are described. The Boltzmann constant is measured in three ways, and the results fall within 15% of the accepted value.
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paper_title: Measuring Boltzmann’s constant using video microscopy of Brownian motion

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Boltzmann’s constant k was measured by observing the Brownian motion of polystyrene spheres in water. An inexpensive monochrome CCD camera and video card were used to create a video of the spheres’ motion. After preprocessing the images, custom routines were used to examine the video and to identify and track the particles from one frame to the next. From the mean squared displacement of the particles versus time, we extracted the value of k from the slope, assuming that the drag force on an individual sphere is well modeled by Stokes’ law. By averaging over 107 particles, we obtained k=(1.415±0.04)×10−23 J/K.
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paper_title: Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper Noise in Digital Images

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Image acquisition is a common task in every image processing operation. Noise is entered (luring image acquisition from its source and once entered it degrades the image and is difficult to remove. In order to achieve the noise cancellation in an image, non-linear filter works better than linear. This paper presents the joint scheme of Wavelet Transform using iterative noise density and Median Filtering to remove Salt and Pepper Noise in Digital Images. The first part of the paper derives the wavelet coefficients with slight increase in noise density and in second part these coefficients are further modified by median filter. The algorithm shows the remarkable improvement over Gaussian noise model and removes most of the noisy part from the image and maintains the visual quality. The level of wavelet decomposition is restricted to three. The renowned indexes Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) demonstrate marked improvement of image denoising over Gaussian method.
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paper_title: Fast restoration of natural images corrupted by high-density impulse noise

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In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures.
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paper_title: Speckle Noise Reduction in Medical Ultrasound Images

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Ultrasound imaging is an incontestable vital tool for diagnosis, it provides in non-invasive manner the internal structure of the body to detect eventually diseases or abnormalities tissues. Unfortunately, the presence of speckle noise in these images affects edges and fine details which limit the contrast resolution and make diagnostic more difficult. In this paper, we propose a denoising approach which combines logarithmic transformation and a non linear diffusion tensor. Since speckle noise is multiplicative and nonwhite process, the logarithmic transformation is a reasonable choice to convert signaldependent or pure multiplicative noise to an additive one. The key idea from using diffusion tensor is to adapt the flow diffusion towards the local orientation by applying anisotropic diffusion along the coherent structure direction of interesting features in the image. To illustrate the effective performance of our algorithm, we present some experimental results on synthetically and real echographic images.
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paper_title: Two-stage image denoising by principal component analysis with local pixel grouping

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This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sample blocks with similar contents are used in the local statistics calculation for PCA transform estimation, so that the image local features can be well preserved after coefficient shrinkage in the PCA domain to remove the noise. The LPG-PCA denoising procedure is iterated one more time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art denoising algorithms.
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paper_title: Image denoising using wavelet transform and wiener filter based on log energy distribution over Poisson-Gaussian noise model

paper_content:

Generally Gaussian noise model is used in image processing applications due to its presence in electronic circuitry used in image acquisition devices like Charge Coupled Device (CCD) cameras. Off let due to discrete nature of electromagnetic radiation in image acquisition process, due to photon noise that follows poison distribution is also exist with Gaussian noise. In this paper we present an algorithm based on Wavelet transform and wiener filter using log energy distribution to denoised digital images corrupted by Poisson-Gaussian noise. Our results clearly demonstrate the robustness of the algorithm over a large range of variance. We have used peak signal to noise ratio (PSNR) for comparison based on filter outlet.
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paper_title: A BRIEF STUDY OF VARIOUS NOISE MODEL AND FILTERING TECHNIQUES

paper_content:

In image processing, noise reduction and restoration of image is expected to improve the qualitative inspection of an image and the performance criteria of quantitative image analysis techniques Digital image is inclined to a variety of noise which affects the quality of image. The main purpose of de-noising the image is to restore the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is corrupting .In the field of reducing the image noise several type of linear and non linear filtering techniques have been proposed . Different approaches for reduction of noise and image enhancement have been considered, each of which has their own limitation and advantages.
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paper_title: A BRIEF STUDY OF VARIOUS NOISE MODEL AND FILTERING TECHNIQUES

paper_content:

In image processing, noise reduction and restoration of image is expected to improve the qualitative inspection of an image and the performance criteria of quantitative image analysis techniques Digital image is inclined to a variety of noise which affects the quality of image. The main purpose of de-noising the image is to restore the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is corrupting .In the field of reducing the image noise several type of linear and non linear filtering techniques have been proposed . Different approaches for reduction of noise and image enhancement have been considered, each of which has their own limitation and advantages.
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Title: A Review Paper: Noise Models in Digital Image Processing

Section 1: INTRODUCTION
Description 1: This section introduces the topic of noise in digital images, its sources, and the significance of understanding noise models for image processing.

Section 2: NOISE MODELS
Description 2: This section gives an overview of different noise models, their sources, and their impact on digital images.

Section 3: Gaussian Noise Model
Description 3: This section describes the Gaussian noise model, its sources, and its mathematical representation.

Section 4: White Noise
Description 4: This section explains white noise, its characteristics, and its implications in digital imaging.

Section 5: Brownian Noise (Fractal Noise)
Description 5: This section discusses Brownian noise, its causes, and its statistical properties including power spectral density.

Section 6: Impulse Valued Noise (Salt and Pepper Noise)
Description 6: This section covers impulse valued noise, its effects on digital images, and the conditions under which it appears.

Section 7: Periodic Noise
Description 7: This section describes periodic noise, its electronic sources, and methods for its removal.

Section 8: Quantization Noise
Description 8: This section explains quantization noise, its origin in the analog-to-digital conversion process, and its distribution.

Section 9: Speckle Noise
Description 9: This section discusses speckle noise, its occurrence in coherent imaging systems, and its gamma distribution characteristics.

Section 10: Photon Noise (Poisson Noise)
Description 10: This section details photon noise arising from statistical fluctuations of electromagnetic waves like x-rays and gamma rays, obeying Poisson distribution.

Section 11: Poisson-Gaussian Noise
Description 11: This section introduces the Poisson-Gaussian noise model, its application in MRI, and its combined statistical characteristics.

Section 12: Structured Noise
Description 12: This section describes structured noise, its periodic and stationary nature, and how it is modeled in a lower dimensionality space.

Section 13: Gamma Noise
Description 13: This section explains gamma noise, its appearance in laser-based images, and its adherence to the Gamma distribution.

Section 14: Rayleigh Noise
Description 14: This section describes Rayleigh noise, its presence in radar range images, and its probability density function.

Section 15: CONCLUSIONS
Description 15: This section concludes the paper by summarizing the importance of noise model studies for effective image denoising and processing.