scholarly journals An Improved Method to Remove Salt and Pepper Noise in Noisy Images

2021 ◽  
Author(s):  
Jinder Kaur ◽  
Gurwinder Kaur ◽  
Ashwani Kumar
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Similarity Index ◽  
Noise Removal ◽  
Mean Value ◽  
Salt And Pepper Noise ◽  
Gray Scale ◽  
Noisy Pixel ◽  
Rgb Images ◽  
Salt And Pepper

In the field of image processing, removal of noise from Gray scale as well as RGB images is an ambitious task. The important function of noise removal algorithm is to eliminate noise from a noisy image. The salt and pepper noise (SPN) is frequently arising into Gray scale and RGB images while capturing, acquiring and transmitting over the insecure several communication mechanisms. In past, the numerous noise removal methods have been introduced to extract the noise from images adulterated with SPN. The proposed work introduces the SPN removal algorithm for Gray scale at low along with high density noise (10\% to 90\%). According to the different conditions of proposed algorithm, the noisy pixel is reconstructed by Winsorized mean or mean value of all pixels except the centre pixel which are present in the processing window. The noise from an image can be removed by using the proposed algorithm without degrading the quality of image. The performance evaluation of proposed and modified decision based unsymmetric median filter (MDBUTMF) is done on the basis of different performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Structure Similarity Index Measurement (SSIM).

scholarly journals AN FPGA IMPLEMENTATION OF MODIFIED DECISION BASED UNSYMMETRICAL TRIMMED MEDIAN FILTER FOR THE REMOVAL OF SALT AND PEPPER NOISE IN DIGITAL IMAGES

2014 ◽  
pp. 159-164
Author(s):  
CHAITANYA BETHINA ◽  
M. PREMKUMAR
Keyword(s):  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Mean Value ◽  
Low Noise ◽  
Color Images ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Salt And Pepper

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. Images are often corrupted by impulse noise during acquisition and transmission; thus, an efficient noise suppression technique is required before subsequent image processing operations. Median filter (MF) is widely used in noise removal methods due to its denoising capability and computational efficiency. However, it is effective only for low noise densities. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than denoising techniques. Especially, the proposed method can preserve edges very well while removing salt and pepper noise. Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF) shows better results at low and medium noise densities. At high noise densities, their performance is poor. A new algorithm to remove high-density salt and pepper noise using modified Decision Based Unsymmetric Trimmed Median Filter (DBUTMF) is proposed. The proposed algorithm replaces the noisy pixel by trimmed median. Since our algorithm is algorithmically simple, it is very suitable to be applied to many real-time applications and higher noise densities. When all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function

2016 ◽  
Vol 6 (5) ◽  
pp. 2219
Author(s):  
Suhad A. Ali ◽  
C. Elaf A. Abbood ◽  
Shaymaa Abdu LKadhm
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Different Types ◽  
Estimation Function ◽  
Salt And Pepper ◽  
Gaussian Estimation

<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

scholarly journals Elimination of Impulse Noise using Mean Median filter for Image Enhancement

2019 ◽  
pp. 278-288
Author(s):  
Trupti Arun Jangale ◽  
Raj Kumar Paul
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Human Perception ◽  
Experimental Result ◽  
Second Step ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
A New Technique ◽  
Salt And Pepper

In this method, we've got introduced a new technique for the improvement of gray scale images, when images are corrupted by salt and pepper noise that's additionally referred to as an impulse noise. Our suggested phenomena show a better output for Medium density impulse noise as compare to the opposite renowned filters like standard Median Filter (SMF), a decision based mostly Median Filter (DBMF) and modified decision based Median Filter (MDBMF), Nonlinear filter (NLF) and so on. Our projected technique worked on two steps, within the beginning is that the detection of noisy pixels and within the second step is that the removal of noisy pixels. For detection of noisy constituent apply condition pixels values lies in between 0 to 255 it noisy it's noisy free pixels. In our second step that's the removal of noisy pixel recommended technique that's replaces the noisy pixel by alpha trimmed mean median value. Different grayscale pictures are tested via proposed technique. The experimental result shows higher Peak Signal to Noise ratio (PSNR) values and with higher visual and human perception.

scholarly journals Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function

2016 ◽  
Vol 6 (5) ◽  
pp. 2219
Author(s):  
Suhad A. Ali ◽  
C. Elaf A. Abbood ◽  
Shaymaa Abdu LKadhm
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Different Types ◽  
Estimation Function ◽  
Salt And Pepper ◽  
Gaussian Estimation

<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

scholarly journals Probabilistic Decision Based Average Trimmed Filter for the Removal of High-Density Salt and Pepper Noise

2020 ◽  
Vol 8 (5) ◽  
pp. 4350-4357
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Absolute Error ◽  
Noise Removal ◽  
Low Noise ◽  
Salt And Pepper Noise ◽  
Ratio Image ◽  
Noise Density ◽  
Chest X Ray ◽  
Salt And Pepper

The paper focuses on the evacuation of salt and pepper noise from a contaminated image. A probabilistic decision based average trimmed filter (PDBATF) is proposed for both high and low noise density. The proposed algorithm addresses the issue related to even number of noise-free pixel in trimmed median filter for the calculation of processing pixel. The proposed average trimmed filter is incorporated for low noise density while the proposed patch else average trimmed filter is applied for high noise density. Finally, they are combined together to develop the proposed PDBATF. The proposed algorithm show an excellent noise removal capability compared to the recently developed algorithms in terms of peak signal to noise ratio, image enhancement factor, mean absolute error and execution time. It works very efficiently in de-noising contaminated medical images such as chest-x-ray and malaria-blood-smear.

Decision Based Median Filter algorithm using Resource Optimized FPGA to Extract Impulse Noise

2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Rutuja Nandkumar Kulkarni ◽  
Pradip C Bhaskar
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Noise Removal ◽  
Mean Value ◽  
Linear Filter ◽  
Noise Detection ◽  
Object Boundary ◽  
Typical Application ◽  
Impulse Noise Removal ◽  
Noisy Pixel

Median filter is a non-linear filter used in image processing for impulse noise removal. It finds its typical application in the situations where edges are to be preserved for higher level operations like segmentation, object recognition etc. This paper presents an accurate and efficient noise detection and filtering algorithm for impulse noise removal. The algorithm includes two stages: noise detection followed by noise filtering. The proposed algorithm replaces the noisy pixel by using  median value when other pixel values, 0’s or 255’s are present in the selected window and when all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. Similarly algorithm checks for five different conditions to preserve image details, object boundary in high level of noise densities. This median filter was designed, simulated and synthesized on the Xilinx family of FPGAs (XC3S500E of Spartan-3E). The VHDL was used to design the above 2-D median filter using ISE (Xilinx) tool &amp; tested &amp; compared for different grayscale images.

scholarly journals High Density Impulse Noise Removal using Advanced Median Filter for Preserving Image Quality Metrics

2019 ◽  
Vol 8 (4) ◽  
pp. 11909-11914
Keyword(s):  
Image Quality ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Quality Metrics ◽  
High Density ◽  
Image Quality Metrics ◽  
Neighboring Pixel ◽  
Salt And Pepper

In this work, a procedure to remove the high density salt and pepper noise from a corrupted image is developed and to compare the output image with the original image through the image quality metrics. As a common practice the corrupted pixels are replaced by the median of neighboring pixel values by considering a constant number of neighboring pixels. But in this proposed method the corrupted pixels are identified and are replaced by the median of the neighboring pixel values which are adjustable, to preserve and improve the image quality metrics. This method makes a comparison between the corrupted and uncorrupted pixels and performs the median filtering process only on the corrupted ones. In this work a 3x3, 5x5 and 7x7 square neighborhood are used. The output images are observed with low neighborhood as well as high neighborhood pixel values. The calculation of PSNR (Peak Signal to Noise Ratio) and MSE (Mean square error) value for each dimension with different percentages are considered for the comparative analysis

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