De-Noising of Binary Image Using Accelerated Local Median-Filtering Approach

2019 ◽  
pp. 164-185
Author(s):  
Amit Khan ◽  
Dipankar Majumdar
Keyword(s):  
Time Complexity ◽  
Window Size ◽  
Analytical Techniques ◽  
Approximate Solutions ◽  
Median Filtering ◽  
Binary Images ◽  
Noise Characteristics ◽  
Varied Size ◽  
Number Of Iterations ◽  
Filtering Approach

In the last few decades huge amounts and diversified work has been witnessed in the domain of de-noising of binary images through the evolution of the classical techniques. These principally include analytical techniques and approaches. Although the scheme was working well, the principal drawback of these classical and analytical techniques are that the information regarding the noise characteristics is essential beforehand. In addition to that, time complexity of analytical works amounts to beyond practical applicability. Consequently, most of the recent works are based on heuristic-based techniques conceding to approximate solutions rather than the best ones. In this chapter, the authors propose a solution using an iterative neural network that applies iterative spatial filtering technology with critically varied size of the computation window. With critical variation of the window size, the authors are able to show noted acceleration in the filtering approach (i.e., obtaining better quality filtration with lesser number of iterations).

Adaptive Median Filtering Based on Unsupervised Classification of Pixels

2013 ◽  
pp. 273-296 ◽  
Author(s):  
J. K. Mandal ◽  
Somnath Mukhopadhyay
Keyword(s):  
Clustering Algorithm ◽  
Digital Images ◽  
Unsupervised Classification ◽  
Median Filtering ◽  
Adaptive Window ◽  
Novel Approach ◽  
Weighted Median ◽  
Window Selection ◽  
Filtering Approach

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital images through unsupervised classification of pixels. This approach coagulates directional weighted median filtering with unsupervised pixel classification based adaptive window selection toward detection and filtering of impulses in digital images. K-means based clustering algorithm has been utilized to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median filtering approach has been proposed to obtain best possible restoration results. Results demonstrating the effectiveness of the proposed technique are provided for numeric intensity values described in terms of feature vectors. Various benchmark digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

PERFORMANCE COMPARISON OF DENOISING METHODS FOR HISTORICAL DOCUMENTS

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Sayed Muchallil ◽  
Fitri Arnia ◽  
Khairul Munadi ◽  
Fardian Fardian
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Performance Comparison ◽  
The Other ◽  
Historical Documents ◽  
Median Filtering ◽  
Document Images ◽  
Post Processing ◽  
Binary Images ◽  
Complete Procedure

Image denoising plays an important role in image processing.  It is also part of the pre-processing technique in a binarization complete procedure that consists of pre-processing, thresholding, and post-processing.  Our previous research has confirmed that the Discrete Cosine Transform (DCT)-based filtering as the new pre-processing process improved the performance of binarization output in terms of recall and precision. This research compares three classical denoising methods; Gaussian, mean, and median filtering with the DCT-based filtering. The noisy ancient document images are filtered using those classical filtering methods. The outputs of this process are used as the input for Otsu, Niblack, Sauvola and NICK binarization methods. Then the resulted binary images of the three classical methods are compared with those of DCT-based filtering. The performance of all denoising algorithms is evaluated by calculating recall and precision of the resulted binary images.  The result of this research is that the DCT based filtering resulted in the highest recall and precision as compared to the other methods. 

scholarly journals Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm

2021 ◽  
Vol 3 (4) ◽  
pp. 284-297
Author(s):  
B. Vivekanandam
Keyword(s):  
Thermal Noise ◽  
Noise Removal ◽  
Common Type ◽  
Imaging Systems ◽  
Research Report ◽  
Median Filtering ◽  
Challenging Problem ◽  
Sensor Devices ◽  
Original Picture ◽  
Filtering Approach

Thermal noise is the most common type of contamination in digital image acquisition operations, and is caused by the temperature condition of the industrial sensor devices used in the process. When it comes to picture improvement, removing noise from the image is one of the most crucial steps. However, in image processing, it is more critical to retain the characteristics of the original picture while eliminating the noise. Thermal noise removal is a challenging problem in image denoising. This article provides a strategy based on a Hybrid Adaptive Median (HAM) filtering approach for removing thermal noise from the image output of an industrial sensor. The demonstration of this proposed approach's ability, is to successfully detect and reduce thermal noise. In addition, this study examines an adaptive hybrid adaptive median filtering approach that has significant computational advantages, making it highly practical. Finally, this research report on experiments shows the high-quality industrial sensor imaging systems that have been successfully implemented in the real world.

scholarly journals Developing and Analyzing Newton – C’otes Quadrature Formulae for Approximating Definite Integrals- AC++ Approach

1970 ◽  
Vol 11 (2) ◽  
pp. 301-316
Author(s):  
Teka Tuemay ◽  
Assefa Dessalegn
Keyword(s):  
Approximate Solutions ◽  
Accurate Solution ◽  
Computational Effort ◽  
Trapezoidal Rule ◽  
Step Size ◽  
C Programs ◽  
Quadrature Formulae ◽  
Definite Integrals ◽  
Short Period ◽  
Number Of Iterations

In this paper, different Newton – C’otes quadrature formulae for the approximation of definite integrals and their error analysis are derived. The order of convergences of the methods is also derived and of these Newton – C’otes quadrature formulae, the Simpson’s 1/3 rule have been shown to have high order of convergence. Since the functionality of these numerical integration methods is practical only if we can use computer programs and applications to produce approximate solutions with acceptable errors within short period, C++ programs for the selected methods are written. These programs are used on the comparison of the Newton – C’otes quadrature formulae and the result obtained based on the inputs and outputs of the programs for different integrands. The results of these programs show that the convergence of the methods highly depends on the number of iterations. The results of different numerical examples show that for high accuracy of the trapezoidal rule computational effort is higher and round off errors with large number of iterations limit the accuracy. The results show that the Simpson’s 1/3 rule produces much more accurate solution than other methods even within small number of iterations. This shows that the error for Simpson’s rule 1/3 converges to zero faster than the error for the trapezoidal rule as the step size decreases. It is finally observed that Simpson’s 1/3 rule is much faster than the Trapezoidal and the Simpson’s 3/8 rules according to the results of the C++ programs.

Impulse Noise Suppression Method by Median Filtering with Parity Extraction

2013 ◽  
Vol 846-847 ◽  
pp. 991-994
Author(s):  
Zhen Xing Li
Keyword(s):  
Impulse Noise ◽  
Noise Suppression ◽  
Median Filter ◽  
Window Size ◽  
New Method ◽  
Median Filtering ◽  
Experiment Data ◽  
Simulation And Experiment ◽  
Suppression Method

A new impulse noise suppression method by median filtering with parity extraction was proposed in this paper. The window size of the median filter has important effect on the performance of the filtering result, larger window size can suppress impulse noise effectively but often at cost of loss of the detail information of the signal, while smaller window size can protect the detail information better but results in degrading of the noise suppression. Parity extraction is done to the signal at first and median filtering carries on the odd and even part respectively, and then a new method of median filtering with short window size to suppress the impulse noise is obtained. Simulation and experiment data of telemetry process results show the effectiveness of the proposed method.

A reduced edge distortion median filtering algorithm for binary images

1990 ◽  
Vol 21 (1) ◽  
pp. 37-47 ◽  
Author(s):  
W.W. Boles ◽  
M. Kanefsky ◽  
M. Simaan
Keyword(s):  
Median Filtering ◽  
Binary Images ◽  
Filtering Algorithm

scholarly journals A Decision-Based Modified Total Variation Diffusion Method for Impulse Noise Removal

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Hongyao Deng ◽  
Qingxin Zhu ◽  
Xiuli Song ◽  
Jinsong Tao
Keyword(s):  
Total Variation ◽  
Impulse Noise ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Diffusion Method ◽  
Median Filtering ◽  
Noise Characteristics ◽  
Weighted Total Variation ◽  
Salt And Pepper

Impulsive noise removal usually employs median filtering, switching median filtering, the total variation L1 method, and variants. These approaches however often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A new method to remove noise is proposed in this paper to overcome this limitation, which divides pixels into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the pixels are divided into corrupted and noise-free; if the image is corrupted by random valued impulses, the pixels are divided into corrupted, noise-free, and possibly corrupted. Pixels falling into different categories are processed differently. If a pixel is corrupted, modified total variation diffusion is applied; if the pixel is possibly corrupted, weighted total variation diffusion is applied; otherwise, the pixel is left unchanged. Experimental results show that the proposed method is robust to different noise strengths and suitable for different images, with strong noise removal capability as shown by PSNR/SSIM results as well as the visual quality of restored images.

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