Study of Noise Removal Techniques for Digital Images

Biometrics ◽  
2017 ◽  
pp. 1105-1144
Author(s):  
Punyaban Patel ◽  
Bibekananda Jena ◽  
Bibhudatta Sahoo ◽  
Pritam Patel ◽  
Banshidhar Majhi
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Statistical Models ◽  
Noise Removal ◽  
Image Degradation ◽  
Linear Filters ◽  
Restoration Techniques ◽  
Filtering Technique ◽  
High Level ◽  
Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Study of Noise Removal Techniques for Digital Images

2016 ◽  
pp. 48-87
Author(s):  
Punyaban Patel ◽  
Bibekananda Jena ◽  
Bibhudatta Sahoo ◽  
Pritam Patel ◽  
Banshidhar Majhi
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Statistical Models ◽  
Noise Removal ◽  
Image Degradation ◽  
Linear Filters ◽  
Restoration Techniques ◽  
Filtering Technique ◽  
High Level ◽  
Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Efficient Noise Removal Based Relevant Vector Machine to Detect Lung Disease

2019 ◽  
Vol 16 (8) ◽  
pp. 3372-3377
Author(s):  
P. Dhanalakshmi ◽  
G. Satyavathy
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Research Work ◽  
Multiple Scale ◽  
Noise Removal ◽  
Semantic Gap ◽  
Linear Filters ◽  
Processing Techniques

The quality of images is decreased by noises. There exist several chances for the noises to occur while capturing and transmission the image. Noise removal becomes a thrust area of research in image processing. The outcome of the noise removal shows the quality of digital image processing techniques. Noises in image lead to the semantic gap problem in medical image processing. Semantic gap problem becomes a serious issue in the classification of the medical image. With the aim to overcome this issue, this research work proposes an efficient noise removal method based on relevant vector machine. Instead of using unsuited linear filters to detect noises, this research work uses the nonlinear filters which suit well to detect noises in multiple scale layers. The proposed method is applied to ADL dataset for the detection of lung cancer. The results clearly show that the proposed noise removal based relevant vector machine performs better in terms of accuracy.

scholarly journals Removal of various Noises in Dental X-ray Images using Selective Median Filter

2019 ◽  
Vol 8 (12) ◽  
pp. 2550-2558
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Median Filter ◽  
Medical Image Analysis ◽  
Wiener Filter ◽  
Speckle Noise ◽  
Linear Filters ◽  
Severe Problem ◽  
X Ray ◽  
Non Linear

Image processing plays major role to provide additional information in medical diagnosis. Input images contain picture information as well as noise information. Noise information is added with the images during signal acquisition stage or in the transmission of image data. Salt & Pepper noise, Gaussian noise and Speckle noise is the major noises introduced in the images. Noise information may be interpreted as data and it may lead to severe problem. Linear and Non-linear filters are used to reduce these noises in the images. In medical image analysis, non-linear filters are preferred over linear filters because it preserves edge information. Dental X-ray image is used to identify the cavities and its depth. Average filter, median filter and wiener filter are the classical techniques used in many image processing applications. In this paper, three different noises (Salt &pepper, Gaussian and Speckle noise) are added and different filters (Average filters, median filter, Wiener filter) performances are analysed with the PSNR, SNR and MSE. Analysis shows that median filter is suitable for reducing salt & pepper noise and wiener filter is suitable for reducing Gaussian noise and speckle noise in the dental x-ray images. Selective median filter is a modified wiener filter. Median filter is used for the pixel value 0 and 255.For other pixel values wiener filter is used. Selective median filter is giving better result than traditional techniques

scholarly journals Implementation of IRIS Image Segmentation using Distance Matrix

2019 ◽  
Vol 8 (12) ◽  
pp. 3845-3850
Keyword(s):  
Image Processing ◽  
Gabor Filter ◽  
Research Work ◽  
Implementation Process ◽  
Distance Matrix ◽  
Noise Removal ◽  
Vital Role ◽  
Classification Algorithms ◽  
Discrete Wavelet ◽  
Filtering Technique

The human reorganization process plays a major part in giving a safer way of information sharing. The security plays a vital role in accessing applications with lot of confidentiality. The image processing can be a solvent of solving various security issues in iris detection technique. The IRIS images are collected from various persons for testing process and compared with various classification algorithms. Images are cleaned with implementing the preprocessing technique for noise removal. The clarity of the image are also improved with implementing image enhancement and resizing process. Basic image processing algorithmic rules are applied for identifying the necessary features for enhanced features. Some of filtering techniques such as Gabor filter, discrete wavelet transformation filter, Gaussian filter, etc, are used for main feature enhancement and filtering technique. These filtering technique used in this research work are helpful in calculating the distance between pupil and iris of an eye. The implementation process is tested with three different dissimilar iris dataset and compared with different classification algorithms. The research work helps in giving a simple solution in giving security to systems as well as giving a solution to identify the eye disease effectively.

scholarly journals Variable Splitting Based Method for Image Restoration with Impulse Plus Gaussian Noise

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Tingting Wu
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Fundamental Problem ◽  
Noise Removal ◽  
Phase Method ◽  
Two Phase ◽  
Variable Splitting ◽  
Mixed Noise ◽  
Mixed Noise Removal

Image denoising is a fundamental problem in realm of image processing. A large amount of literature is dedicated to restoring an image corrupted by a certain type of noise. However, little literature is concentrated on the scenario of mixed noise removal. In this paper, based on the model of two-phase method for image denoising proposed by Cai et al. (2008) and the idea of variable splitting, we are capable of decomposing the image denoising problem into subproblems with closed form. Numerical results illustrate the validity and robustness of the proposed algorithms, especially for restoring the images contaminated by impulse plus Gaussian noise.

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

2015 ◽  
Vol 14 (02) ◽  
pp. 1550017
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Classical Problem ◽  
Statistical Parameter ◽  
Noise Model ◽  
Natural Image ◽  
Bayesian Techniques ◽  
Generalized Gamma ◽  
Gamma Density

The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

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