Periodic noise removal using local thresholding

2016 ◽  
Author(s):  
Vipin Prakash Yadav ◽  
Gajendra Singh ◽  
Md. Imtiyaz Anwar ◽  
Arun Khosla
Keyword(s):  
Noise Removal ◽  
Periodic Noise ◽  
Local Thresholding

Perception Thresholding for Noise Removal in Micrographs of Cellular Tissues Acquired by Fluorescence Microscopy

2018 ◽  
Author(s):  
Saad Manzur ◽  
Md. Badiul Haque Shawon ◽  
Mahmuda Naznin ◽  
Tanvir R. Faisal
Keyword(s):  
Spatial Modeling ◽  
Woody Plant ◽  
Similarity Index ◽  
Hierarchical Structures ◽  
Noise Removal ◽  
Cellular Tissue ◽  
Experimental Procedure ◽  
Local Thresholding ◽  
Otsu’S Method ◽  
A Cell

Plant petioles and stems are hierarchical structures comprising cellular tissues in one or more intermediate hierarchies displaying quasi random to heterogeneous cellularity that governs the overall structural properties. Exact replication of natural cellular tissue leads to the investigation of mechanical properties at the microstructural level. However, the micrographs often display artifacts due to experimental procedure and prevent representative spatial modeling of the tissues. Existing methods such as local thresholding or global thresholding (Otsu’s method) fail to effectively remove the artifacts. Hence, an efficient algorithm is required that can effectively help to reconstruct the geometric models of tissue microstructures by removing the noise. In this work, perception-based thresholding that conceptually works like human brain in differentiating noise from the actual ones based on color is introduced to remove discrete (within a cell) or adjacent (to the cell boundaries) noise. A variety of image dataset of non-woody plant tissues were tested with the algorithm, and its effectiveness in eliminating noise was quantitatively compared with existing noise removal techniques by Bivariate Similarity Index. The bivariate metrics indicate an enhanced performance of the perception-based thresholding over other considered algorithms.

scholarly journals Periodic Noise Removal in Strain and Natural Images Using 2-D Fast Fourier Transform

2013 ◽  
pp. 90-94
Author(s):  
Mandeep Kaur ◽  
Dinesh Kumar ◽  
Ekta Walia ◽  
Manjit Sandhu
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Frequency Domain ◽  
Noise Removal ◽  
Natural Images ◽  
Periodic Pattern ◽  
Periodic Noise ◽  
Mean Filter ◽  
The Mean

This paper presents a 2-D FFT removal algorithm for reducing the periodic noise in natural and strain images. For the periodic pattern of the artifacts, we apply the 2-D FFT on the strain and natural images to extract and remove the peaks which are corresponding to periodic noise in the frequency domain. Further the mean filter applied to get more effective results. The performance of the proposed method is tested on both natural and strain images. The results of proposed method is compared with the mean filter based periodic noise removal and found that the proposed method significantly improved for the noise removal.

A windowed Gaussian notch filter for quasi-periodic noise removal

2008 ◽  
Vol 26 (10) ◽  
pp. 1347-1353 ◽  
Author(s):  
Igor Aizenberg ◽  
Constantine Butakoff
Keyword(s):  
Notch Filter ◽  
Noise Removal ◽  
Periodic Noise

scholarly journals WAVELET AND FOURIER DECOMPOSITION BASED PERIODIC NOISE REMOVAL IN MICROSCOPY IMAGES

2018 ◽  
Vol 18 (1) ◽  
pp. 68-71
Author(s):  
M.G. Ionita ◽  
H.G. Coanda
Keyword(s):  
Haar Wavelet ◽  
Adaptive Method ◽  
Noise Removal ◽  
Haar Wavelet Transform ◽  
Fourier Decomposition ◽  
Magnitude Spectrum ◽  
First Order ◽  
Periodic Noise ◽  
Quality Degradation ◽  
Microscopy Images

Abstract The microscopy images can be affected by periodic noise, resulting in quality degradation and the appearance of repetitive patterns on the micrograph. In order to effectively remove the periodic noise, a new adaptive method is proposed in this paper. The presented approach analyzes the frequency domain of the first order Haar Wavelet transform, determines the frequency regions that correspond to noise and corrects the magnitude spectrum components of the undecomposed DFT transform of the image. Experimental results are provided.

Practical Methods for Noise Removal: Applications to Spikes, Nonstationary Quasi-Periodic Noise, and Baseline Drift

2009 ◽  
Vol 81 (12) ◽  
pp. 4987-4994 ◽  
Author(s):  
Delphine Feuerstein ◽  
Kim H. Parker ◽  
Martyn G. Boutelle
Keyword(s):  
Noise Removal ◽  
Baseline Drift ◽  
Periodic Noise
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