ESTIMATION AND REDUCTION OF GAUSSIAN NOISE AND HIGHDENSITY SALT AND PEPPER NOISE THROUGH MODIFIED DECISION BASED TRIMMED MEDIAN AND GAUSSIAN FILTER USING MATLAB

2016 ◽  
Vol 3 (3) ◽  
pp. 24 ◽  
Author(s):  
TIWARI APOORWA ◽  
DEVANAND BHONSLE ◽  
◽  
Keyword(s):  
Gaussian Noise ◽  
Gaussian Filter ◽  
Salt And Pepper Noise ◽  
Salt And Pepper

scholarly journals Comparing U-Net Based Models for Denoising Color Images

AI ◽  
2020 ◽  
Vol 1 (4) ◽  
pp. 465-487
Author(s):  
Rina Komatsu ◽  
Tad Gonsalves
Keyword(s):  
Deep Learning ◽  
Performance Improvement ◽  
Gaussian Noise ◽  
Salt And Pepper Noise ◽  
L1 Norm ◽  
Specific Kind ◽  
Wide Interface ◽  
Salt And Pepper ◽  
Deep Learning Model ◽  
And Training

Digital images often become corrupted by undesirable noise during the process of acquisition, compression, storage, and transmission. Although the kinds of digital noise are varied, current denoising studies focus on denoising only a single and specific kind of noise using a devoted deep-learning model. Lack of generalization is a major limitation of these models. They cannot be extended to filter image noises other than those for which they are designed. This study deals with the design and training of a generalized deep learning denoising model that can remove five different kinds of noise from any digital image: Gaussian noise, salt-and-pepper noise, clipped whites, clipped blacks, and camera shake. The denoising model is constructed on the standard segmentation U-Net architecture and has three variants—U-Net with Group Normalization, Residual U-Net, and Dense U-Net. The combination of adversarial and L1 norm loss function re-produces sharply denoised images and show performance improvement over the standard U-Net, Denoising Convolutional Neural Network (DnCNN), and Wide Interface Network (WIN5RB) denoising models.

Feature Extraction from Noisy Image Using Intersecting Cortical Model

2010 ◽  
Vol 40-41 ◽  
pp. 516-522 ◽  
Author(s):  
Xiao Fei Wang ◽  
Bo Nian Li ◽  
Yan Li Huang ◽  
Xin Ran Wang
Keyword(s):  
Feature Extraction ◽  
Gaussian Noise ◽  
Feature Vector ◽  
Input Image ◽  
Noisy Image ◽  
Salt And Pepper Noise ◽  
Cortical Model ◽  
Gray Image ◽  
The Mean ◽  
Salt And Pepper

This paper introduces an efficient approach for feature extraction from noisy image using Intersecting Cortical Model(ICM), which is a simplified model of Pulse-Coupled Neural Network(PCNN). In our research, the entropy sequence of the output image, is obtained from the original gray image by ICM, as feature vector of the gray image, which can be used to represent the gray image, and this has been proved by our experiments. Consequently, it is used in the image classification, and the mean square error (MSE) between the feature vector of the input image and the standard feature vector is used to judge to which image groups the input image belongs. It has been proved that the method is not sensitivity with the Gaussian noise, salt and pepper noise or both of this and greatly robust for image recognition.

scholarly journals Noise Removal and Filtering Techniques used in Medical Images

2017 ◽  
Vol 10 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Nalin Kumar ◽  
M Nachamai
Keyword(s):  
Medical Imaging ◽  
Gaussian Noise ◽  
Medical Images ◽  
Median Filter ◽  
Research Work ◽  
Noise Removal ◽  
Poisson Noise ◽  
Gaussian Filter ◽  
Gray Scale ◽  
Salt And Pepper

Noise removal techniques have become an essential practice in medical imaging application for the study of anatomical structure and image processing of MRI medical images. To report these issues many de-noising algorithm has been developed like Weiner filter, Gaussian filter, median filter etc. In this research work is done with only three of the above filters which are already mentioned were successfully used in medical imaging. The most commonly affected noises in medical MRI image are Salt and Pepper, Speckle, Gaussian and Poisson noise. The medical images taken for comparison include MRI images, in gray scale and RGB. The performances of these algorithms are examined for various noise types which are salt-and-pepper, Poisson, speckle, blurred and Gaussian Noise. The evaluation of these algorithms is done by the measures of the image file size, histogram and clarity scale of the images. The median filter performs better for removing salt-and-pepper noise and Poisson Noise for images in gray scale, and Weiner filter performs better for removing Speckle and Gaussian Noise and Gaussian filter for the Blurred Noise as suggested in the experimental results.

An Image Filtering Algorithm for Removing Mixed Noise

2013 ◽  
Vol 433-435 ◽  
pp. 383-388 ◽  
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.

scholarly journals De-Noising of Mango fruits using Mean Convolution Mass Filter (MCMF)

2019 ◽  
Vol 8 (3) ◽  
pp. 3015-3018
Keyword(s):  
Gaussian Noise ◽  
Visual Information ◽  
The Other ◽  
Salt And Pepper Noise ◽  
Mass Filter ◽  
High Quality ◽  
Modern Age ◽  
High Quality Image ◽  
Quality Image ◽  
Salt And Pepper

In this modern age transmission of visual information in the form of digital images are becoming a major method of communication. The noise is the result of errors in image acquisition process and they do not replicate the accurate intensities of the actual scene. This image can be utilized as an input for decision making. To produce high quality image it should be de-noised using suitable algorithm. There are various types of noises that degrades the image such as salt and pepper noise, Gaussian noise and Poisson noise, it is necessary to have knowledge about the noise present in the image. The paper proposed a new algorithm “Mean Convolution Mass Filter (MCMF)”. This approach has been proved to have better PSNR value in de-noising the digital image when compared to the other existing algorithms

Blind Watermarking Algorithm for Color Images Based on Nearest Neighbor Interpolation

2013 ◽  
Vol 333-335 ◽  
pp. 1185-1191
Author(s):  
Xin Guo Zou
Keyword(s):  
Gaussian Noise ◽  
Nearest Neighbor ◽  
Color Image ◽  
Intermediate Frequency ◽  
Wiener Filtering ◽  
Blind Watermarking ◽  
Salt And Pepper Noise ◽  
Strong Robustness ◽  
Carrier Image ◽  
Salt And Pepper

In this paper, we present a blind digital watermarking method for color image based on nearest neighbor interpolation. The algorithm has the following characteristics: (1) it generate adaptive watermark based on the size of carrier image,so as to improve the robustness of binary watermark; (2) the watermark is embedded in the DCT coefficient of intermediate frequency, which can effectively resist common attacks, such as Gaussian noise-adding, salt and pepper noise-adding, cropping, Wiener filtering, JPEG compression;(3) the watermarking detection is simple and fast, and does not require carrier image. The experiments show that the algorithm has good invisibility and strong robustness.

scholarly journals An Improved Image Filtering Algorithm for Mixed Noise

2021 ◽  
Vol 11 (21) ◽  
pp. 10358
Author(s):  
Chun He ◽  
Ke Guo ◽  
Huayue Chen
Keyword(s):  
Gaussian Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Block Matching ◽  
Threshold Function ◽  
Median Filtering ◽  
Salt And Pepper Noise ◽  
Filtering Algorithm ◽  
Mixed Noise ◽  
Salt And Pepper

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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