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%.

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.

De-noising of images from salt and pepper noise using hybrid filter, fuzzy logic noise detector and genetic optimization algorithm (HFGOA)

2019 ◽  
Vol 79 (5-6) ◽  
pp. 4115-4131 ◽  
Author(s):  
A. Senthil Selvi ◽  
K. Pradeep Mohan Kumar ◽  
S. Dhanasekeran ◽  
P. Uma Maheswari ◽  
S. Ramesh ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Optimization Algorithm ◽  
Genetic Optimization ◽  
Salt And Pepper Noise ◽  
Hybrid Filter ◽  
Salt And Pepper ◽  
Genetic Optimization Algorithm

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 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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