Low-Frequency Image Noise Removal Using White Noise Filter

2018 ◽  
Author(s):  
Meisam Rakhshanfar ◽  
Maria A. Amer
Keyword(s):  
White Noise ◽  
Low Frequency ◽  
Noise Removal ◽  
Image Noise ◽  
Noise Filter

An image-noise filter with emphasis on low-frequency chrominance noise

2009 ◽  
Author(s):  
Radu V. Gheorghe ◽  
Sergiu R. Goma ◽  
Milivoje Aleksic
Keyword(s):  
Low Frequency ◽  
Image Noise ◽  
Noise Filter

scholarly journals Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Zhou ◽  
ZhenHong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Image Enhancement ◽  
Fuzzy Set ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Low Frequency ◽  
Noise Removal ◽  
Adaptive Threshold ◽  
Image Noise ◽  
Medical Image Enhancement

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.

scholarly journals A Parallel Method for Impulsive Image Noise Removal on Hybrid CPU/GPU Systems

2013 ◽  
Vol 18 ◽  
pp. 2504-2507 ◽  
Author(s):  
M.G. Sánchez ◽  
V. Vidal ◽  
J. Bataller ◽  
J. Arnal
Keyword(s):  
Noise Removal ◽  
Image Noise ◽  
Parallel Method

scholarly journals Denoising of Ecg Signals Using Fir & Iir Filter: a Performance Analysis

2018 ◽  
Vol 7 (4.12) ◽  
pp. 1
Author(s):  
Dr. Chhavi Saxena ◽  
Dr. Avinash Sharma ◽  
Dr. Rahul Srivastav ◽  
Dr. Hemant Kumar Gupta
Keyword(s):  
Low Frequency ◽  
Noise Removal ◽  
Ecg Signal ◽  
Clinical Practices ◽  
Iir Filters ◽  
Ecg Signals ◽  
Iir Filter ◽  
Ecg Signal Processing ◽  
Movement Artifacts ◽  
Electric Signals

Electrocardiogram (ECG) signal is the electrical recording of coronary heart activity. It is a common routine and vital cardiac diagnostic tool in which in electric signals are measured and recorded to recognize the practical status of heart, but ECG signal can be distorted with noise as, numerous artifacts corrupt the unique ECG signal and decreases it quality. Consequently, there may be a need to eliminate such artifacts from the authentic signal and enhance its quality for better interpretation. ECG signals are very low frequency signals of approximately 0.5Hz-100Hz and digital filters are used as efficient approach for noise removal of such low frequency signals. Noise may be any interference because of movement artifacts or due to power device that are present wherein ECG has been taken. Consequently, ECG signal processing has emerged as a common and effective tool for research and clinical practices. This paper gives the comparative evaluation of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.  

Image Quality Assessment of Low-Dose CT using Hybrid Iterative Reconstruction

2021 ◽  
Author(s):  
Qiao Zhang ◽  
Jinhua Sheng ◽  
Bin Chen
Keyword(s):  
Computed Tomography ◽  
Noise Reduction ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Low Frequency ◽  
Image Noise ◽  
Observer Performance ◽  
Human Observer ◽  
X Ray ◽  
Hybrid Iterative Reconstruction

Background: X-ray computed tomography is the first imaging technology that supports accurate nondestructive interior image reconstruction of an object from sufficient projection data. Low-dose computed tomography (LDCT) has been considered to relieve the harm to patients caused by X-ray radiation. However, LDCT images can be degraded by quantum noise and streak artifacts. Methods: The objective of the authors’ study is to evaluate the optimal level of the hybrid iterative reconstruction (HIR) that generates images with the best diagnostic quality on different dose and noise levels. HIR with optimizations is proposed to reduce image noise and provide better performance at a low dose. The Catphan R 504 phantom is employed to assess various image qualities (IQ). Results: For any given scanning protocols, there is linear noise reduction and linear increase of contrast-to- noise ratio (CNR) using optimal HIR. The evidence from various module tests demonstrates that the shape of the noise power spectrum is continuously shifted to low frequency with increasing HIR levels compared with that of filtered-back-projection (FBP). This may describe the difference between the human observer performance and features of the ideal low-contrast objects. Conclusion: Optimal HIR is clearly demonstrated to be a superior method for reducing image noise and improving CNR compared to FBP. Optimal HIR also inhibits texture change or spectrum shift compared with the pure IR method. Even though there are continuous noise reduction and CNR increase with HIR at increasing levels, the human observer performance does not seem to improve simultaneously due to coarser noise (low-frequency noise). HIR level 3 to 5 is optimal for their study. It is possible for the optimal HIR to offer equivalent diagnostic IQ at a lower dose compared with FBP at a routine dose.

scholarly journals Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 938
Author(s):  
Hyunho Choi ◽  
Jechang Jeong
Keyword(s):  
Additive Noise ◽  
Low Frequency ◽  
Speckle Noise ◽  
Image Filtering ◽  
Noise Removal ◽  
Discrete Wavelet ◽  
Ultrasound Images ◽  
Noise Elimination ◽  
Residual Noise ◽  
Guided Image Filtering

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.

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