Dual Ascent Based Median Filter for Image Restoration

2019 ◽  
Author(s):  
Narendra Kumar ◽  
H. S. Shukla ◽  
Arvind Kumar Tiwari ◽  
Anil Kumar Dahiya
Keyword(s):  
Image Restoration ◽  
Median Filter ◽  
Dual Ascent

Reduction of Noise in Restoration of Images Using Mean and Median Filtering Techniques

2021 ◽  
Vol 9 (9) ◽  
pp. 301-313
Author(s):  
Vimal Chauhan
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Median Filter ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Digital Cameras ◽  
Salt And Pepper Noise ◽  
Satellite Imaging ◽  
Mean Filter ◽  
Salt And Pepper

Abstract: The purpose of this paper is to present a study of digital technology approaches to image restoration. This process of image restoration is crucial in many areas such as satellite imaging, astronomical image & medical imaging where degraded images need to be repaired Personal images captured by various digital cameras can easily be manipulated by a variety of dedicated image processing algorithms [2]. Image restoration can be described as an important part of image processing technique. Image restoration has proved to be an active field of research in the present days. The basic objective is to enhance the quality of an image by removing defects and make it look pleasing [2]. In this paper, an image restoration algorithm based on the mean and median calculation of a pixel has been implemented. We focused on a certain iterative process to carry out restoration. The algorithm has been tested on different images with different percentage of salt and pepper noise. The improved PSNR and MSE values has been obtained. Keywords: De-Noising, Image Filtering, Mean Filter & Median Filter, Salt and Pepper Noise, Denoising Techniques, Image Restoration.

scholarly journals Eliminating Non-Uniform Smearing and Suppressing the Gibbs Effect on Reconstructed Images

2020 ◽  
Author(s):  
Valery Sizikov ◽  
Aleksandra Dovgan ◽  
Aleksei Lavrov
Keyword(s):  
Image Restoration ◽  
Adaptive Filter ◽  
General Type ◽  
Median Filter ◽  
Convolution Type ◽  
Fredholm Integral Equations ◽  
Ill Posed ◽  
Computer Based ◽  
The Fourier Transform

In this work, the problem is considered for eliminating a non-uniform rectilinear smearing of an image by mathematical and computer-based way, for example, a picture of several cars taken with a fixed camera and moving with different speeds. The problem is described by a set of 1-dimensional Fredholm integral equations (IEs) of the first kind of convolution type with a 1-dimensional point spread function (PSF) at uniform smearing and by a set of new 1-dimensional IEs of a general type (not convolution type) with a 2-dimensional PSF at non-uniform smearing. The problem is also described by one 2-dimensional IE of convolution type with a 2-dimensional PSF at uniform smearing and by a new 2-dimensional IE of a general type with a 4-dimensional PSF at non-uniform smearing. The problem for solving Fredholm IE of the first kind is ill-posed (unstable). Therefore, IEs of convolution type are solved by the Fourier transform (FT) method and Tikhonov's regularization (TR), and IEs of general type are solved by the quadrature/cubature and TR methods. Moreover, the magnitude of the image smear Δ is determined by the original “spectral method”, which increases the accuracy of image restoration. It is shown that the use of a set of 1-dimensional IEs is preferable to one 2-dimensional IE in the case of non-uniform smearing. In the inverse problem (image restoration), the Gibbs effect (the effect of false waves) in the image may occur. It can be edge or inner. The edge effect is well suppressed by the proposed technique “diffusing the edges". In the case of an inner effect, it is eliminated with difficulty, but the image smearing itself plays the role of diffusing and suppresses the inner Gibbs effect to a large extent. It is shown (in the presence of impulse noise in an image) that the well-known Tukey median filter can distort the image itself, and the Gonzalez adaptive filter also distorts the image (but to a lesser extent). We propose a modified adaptive filter. A software package was developed in MatLab and illustrative calculations are performed.

Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images

2018 ◽  
Vol 11 (3) ◽  
pp. 47-61 ◽  
Author(s):  
Xin-Ming Zhang ◽  
Qiang Kang ◽  
Jin-Feng Cheng ◽  
Xia Wang
Keyword(s):  
Image Restoration ◽  
Performance Improvement ◽  
Adaptive Filtering ◽  
Median Filter ◽  
Window Size ◽  
Salt And Pepper Noise ◽  
Noise Detection ◽  
Median Filters ◽  
Simple Logic ◽  
Salt And Pepper

In order to accelerate denoising and improve the denoising performance of the current median filters, an Adaptive Four-dot Median Filter (AFMF) for image restoration is proposed in this article. AFMF is not only very efficient and fast in logic execution, but also it can restore the corrupted images with 1–99% densities of salt-and-pepper noise to the satisfactory ones. Without any complicated operation for noise detection, it intuitively and simply distinguishes impulse noises, while keeping the noise-free pixels intact. Only the uncorrupted pixels of the four-dot mask in adaptive filtering windows are used for the adoption of candidates for median finding, whatever filtering window size is. Furthermore, the adoption of recursive median filters leads to denoising performance improvement and faster filtering. The simple logic of the proposed algorithm obtains significant milestones on the fidelity of a restored image. Relevant experimental results on subjective visualization and objective digital measure validate the robustness of the proposed filter.

Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration

2004 ◽  
Vol 16 (2) ◽  
pp. 333-354 ◽  
Author(s):  
Tzu-Chao Lin ◽  
Pao-Ta Yu
Keyword(s):  
Support Vector Machines ◽  
Image Restoration ◽  
Impulse Noise ◽  
Median Filter ◽  
Support Vector ◽  
New Approach ◽  
Median Filters ◽  
Vector Machines ◽  
Impulse Detector

In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.

scholarly journals Eliminating Nonuniform Smearing and Suppressing the Gibbs Effect on Reconstructed Images

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 30
Author(s):  
Valery Sizikov ◽  
Aleksandra Dovgan ◽  
Aleksei Lavrov
Keyword(s):  
Image Restoration ◽  
Adaptive Filter ◽  
General Type ◽  
Median Filter ◽  
Convolution Type ◽  
Fredholm Integral Equations ◽  
Two Dimensional ◽  
One Dimensional ◽  
Ill Posed ◽  
The Fourier Transform

In this work, the problem of eliminating a nonuniform rectilinear smearing of an image is considered, using a mathematical- and computer-based approach. An example of such a problem is a picture of several cars, moving with different speeds, taken with a fixed camera. The problem is described by a set of one-dimensional Fredholm integral equations (IEs) of the first kind of convolution type, with a one-dimensional point spread function (PSF) when uniform smearing, and by a set of new one-dimensional IEs of a general type (i.e., not the convolution type), with a two-dimensional PSF when nonuniform smearing. The problem is also described by a two-dimensional IE of the convolution type with a two-dimensional PSF when uniform smearing, and by a new two-dimensional IE of a general type with a four-dimensional PSF when nonuniform smearing. The problem of solving the Fredholm IE of the first kind is ill-posed (i.e., unstable). Therefore, IEs of the convolution type are solved by the Fourier transform (FT) method and Tikhonov’s regularization (TR), and IEs of the general type are solved by the quadrature/cubature and TR methods. Moreover, the magnitude of the image smear, Δ, is determined by the original “spectral method”, which increases the accuracy of image restoration. It is shown that the use of a set of one-dimensional IEs is preferable to one two-dimensional IE in the case of nonuniform smearing. In the inverse problem (i.e., image restoration), the Gibbs effect (the effect of false waves) in the image may occur. This may be an edge or an inner effect. The edge effect is well suppressed by the proposed technique, namely, “diffusing the edges”. The inner effect is difficult to eliminate, but the image smearing itself plays the role of diffusion and suppresses the inner Gibbs effect to a large extent. It is shown (in the presence of impulse noise in an image) that the well-known Tukey median filter can distort the image itself, and the Gonzalez adaptive filter also distorts the image (but to a lesser extent). We propose a modified adaptive filter. A software package was developed in MATLAB and illustrative calculations are performed.

scholarly journals ANALISIS PERBANDINGAN METODE FILTER MEAN, MEDIAN, MAXIMUM, MINIMUM, DAN GAUSSIAN TERHADAP REDUKSI NOISE GAUSSIAN, SALT&PAPPER , SPECKLE, POISSON, DAN LOCALVAR

2019 ◽  
Vol 17 (1) ◽  
pp. 15
Author(s):  
I Gede Aris Gunadi
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Median Filter ◽  
Improve Image Quality ◽  
Mean Filter ◽  
The Right ◽  
Influence Of Noise ◽  
Median Maximum ◽  
Maximum Minimum

Due to the influence of noise on an image, the image will experience a decrease in quality.  If the type of noise is known for certain, then the right solution can be determined to restore the condition of an image so that the condition returns to normal. The effort to restore the image condition is stated by image restoration. The most important thing in image restoration is determining the type of noise and the solution for the noise.In this study several types of noise were tried,  gaussian, salt & paper, speckle, poisson, and Localvar on several image samples. In the image that had been exposed to noise, repairs were carried out with several types of filters including gaussian, mean, median, maximum, and minimum. Next was the quality of noise reduction with each filter  determined based on the value of PSNR and MSE. The results of image restoration experiments showed that the mean filter was the best filter used to improve noisegaussian, salt & peppers and speckle image quality. The median filter is the filter that is best used to improve image quality with poisson and localvar noise types.  

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