An Improved Image Filtering Algorithm for Mixed Noise

In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.

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An Image Filtering Algorithm for Removing Mixed Noise

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.383 ◽

2013 ◽

Vol 433-435 ◽

pp. 383-388 ◽

Cited By ~ 1

Author(s):

Mao Xiang Chu ◽

An Na Wang ◽

Rong Fen Gong

Keyword(s):

Gaussian Noise ◽

Weighting Function ◽

Median Filter ◽

Bilateral Filter ◽

Salt And Pepper Noise ◽

Filtering Algorithm ◽

Noise Simulation ◽

Mixed Noise ◽

Local Edge ◽

Salt And Pepper

In order to remove salt-and-pepper noise and Gaussian noise in image, a novel filtering algorithm is proposed in this paper. The novel algorithm can preserve image edge details as much as possible. Firstly, five-median-binary code (FMBC) is proposed and used to describe local edge type of image. Secondly, median filter algorithm is improved to remove salt-and-pepper noise by using FMBC. Then, local enhanced bilateral filter with FMBC and a new type of exponential weighting function is used to remove Gaussian noise. Simulation results show that the algorithm proposed in this paper is very effective not only in filtering mixed noise but also in preserving edge details.

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A new threshold-based median filtering technique for salt and pepper noise removal

10.1117/12.856333 ◽

2010 ◽

Cited By ~ 1

Author(s):

Geeta Hanji ◽

M. V. Latte

Keyword(s):

Noise Removal ◽

Median Filtering ◽

Salt And Pepper Noise ◽

Filtering Technique ◽

Salt And Pepper

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Reconstruction of Corrupted Image From Salt And Pepper Noise From Median Filter

Undergraduate Academic Research Journal ◽

10.47893/uarj.2012.1002 ◽

2012 ◽

pp. 4-6

Author(s):

Vishal Gautam ◽

Tarun Varma

Keyword(s):

Median Filter ◽

Median Filtering ◽

Salt And Pepper Noise ◽

Filtering Algorithm ◽

Noise Density ◽

Mean Filter ◽

Salt And Pepper ◽

Image Details ◽

Lower Noise ◽

Better Than

- Inthis paper,we propose an improved median filtering algorithm. Here, we introduced salt and pepper noise for the image corruption and reconstruct original image using different filters i.e. mean, median and improved median filter. The performance of improved median filter is good at lower noise density levels.The mean filter suppresses little noise and gets the worst results.The experimental resultsshow that our improved median filter is better than previousmedian filterfor lower noise density (upto 60%). It removes most of the noises effectively while preserving image details very well.

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Digital Image Filtering Techniques- A survey

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v10i6.7024 ◽

2013 ◽

Vol 10 (6) ◽

pp. 1738-1742

Author(s):

Sakshi Mahajan ◽

Navneet Bawa

Keyword(s):

Digital Image ◽

Image Filtering ◽

Median Filtering ◽

Salt And Pepper Noise ◽

Main Emphasis ◽

Comparison Table ◽

Salt And Pepper ◽

Filtering Techniques

This paper presents a review on digital image filtering techniques. The main emphasis is on median filtering and its extended versions like hybrid median filtering, relaxed median filtering etc. It is found that still median filtering demands some sort of enhancements as it is best for salt and pepper noise only. By conducting a survey suitable gaps are found in existing literature. In the end comparison table is also drawn among the existing techniques.

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Removing of Salt-and-pepper Noise in Images Based on Adaptive Median Filtering and Improved Threshold Function

2019 Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2019.8832612 ◽

2019 ◽

Author(s):

Ying Qian

Keyword(s):

Threshold Function ◽

Median Filtering ◽

Salt And Pepper Noise ◽

Salt And Pepper

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Ameliorated mean adaptive median filtering algorithm

E3S Web of Conferences ◽

10.1051/e3sconf/201911802069 ◽

2019 ◽

Vol 118 ◽

pp. 02069

Author(s):

Hongming Zhang ◽

Yongping Wang ◽

Chuang Peng

Keyword(s):

Median Filter ◽

Random Noise ◽

Infrared Image ◽

Electrical Equipment ◽

Median Filtering ◽

Salt And Pepper Noise ◽

Filtering Algorithm ◽

Average Value ◽

Adaptive Median Filter ◽

Salt And Pepper

Aiming at the problem that the quality of infrared image decreases due to the large amount of random noise in the process of collection and transmission of infrared image of electrical equipment, and the accuracy of automatic detection of electrical equipment decreases, based on the traditional adaptive median filter algorithm, the adaptive median filter is analyzed, which can filter only the salt and pepper noise below 25%. An improved mean adaptive median filtering algorithm is proposed to overcome the shortcomings of wave effect. Firstly, the filtering window is selected according to the decision setting condition, and then it is judged whether the K-mean value near the center point is a noise point, and if so, the window is increased, otherwise the average value is output. Finally, it is judged whether the value of the current pixel point is noise, and if so, the average value is output, otherwise, the current pixel value is output. The experimental results show that the algorithm can effectively filter salt and pepper noise and Gauss noise, while maintaining the image sharpness, and has good filtering performance on PSNR and MSE indicators.

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