scholarly journals NAMF: A Nonlocal Adaptive Mean Filter for Removal of Salt-and-Pepper Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Houwang Zhang ◽  
Yuan Zhu ◽  
Hanying Zheng
Keyword(s):  
Experimental Results ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Mean Filter ◽  
Salt And Pepper ◽  
Novel Algorithm

In this paper, a novel algorithm called a Nonlocal Adaptive Mean Filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise. The noisy pixel is then replaced by the combination of its neighboring pixels, and finally, a SAP noise based nonlocal mean filter is used to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that NAMF can obtain better performance in terms of quality for restoring images at all levels of SAP noise.

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 Segmentation of rays in wood microscopy images using the U-net model

BioResources ◽  
2020 ◽  
Vol 16 (1) ◽  
pp. 721-728
Author(s):  
Halime Ergun
Keyword(s):  
Optimization Algorithm ◽  
Input Image ◽  
Tangential Section ◽  
Salt And Pepper Noise ◽  
Anatomical Feature ◽  
Segmentation Accuracy ◽  
Mean Filter ◽  
Salt And Pepper ◽  
First Time ◽  
Microscopy Images

Rays are an important anatomical feature in tree species identification. They are found in certain proportions in trees, which vary for each tree. In this study, the U-Net model is adopted for the first time to detect wood rays. A dataset is created with images taken from the wood database. The resolution of microscopic wood images in tangential section is 640×400. The input image for training is divided into 32×32 image blocks. Each pixel in the dataset is labeled as belonging to the ray or the background. Then, the dataset is increased by applying scale, rotation, salt-and-pepper noise, circular mean filter, and gauss filter. The U-Net network created for ray segmentation is trained using the Adam optimization algorithm. The experimental results show that the ray segmentation accuracy in testing is 96.3%.

scholarly journals Research on Noise Reduction and Enhancement Algorithm of Girth Weld Image

2021 ◽  
Vol 12 (1) ◽  
pp. 9-21
Author(s):  
Xiang-Song Zhang ◽  
Wei-Xin Gao ◽  
Shi-Ling Zhu
Keyword(s):  
Kernel Function ◽  
Experimental Results ◽  
Gaussian Kernel ◽  
Enhancement Effect ◽  
Salt And Pepper Noise ◽  
Central Pixel ◽  
Local Mean ◽  
Non Local ◽  
Mixed Noise ◽  
Salt And Pepper

In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.

scholarly journals Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal

2019 ◽  
Vol 78 (24) ◽  
pp. 35401-35418 ◽  
Author(s):  
Serdar Enginoğlu ◽  
Uğur Erkan ◽  
Samet Memiş
Keyword(s):  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Riesz Mean ◽  
Mean Filter ◽  
Salt And Pepper
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