A Hybrid Filtering Algorithm for Pantograph Image Denoising

2018 ◽  
pp. 409-417
Author(s):  
Weiwei Pei ◽  
Zongyi Xing ◽  
Zhuang Chen
Keyword(s):  
Image Denoising ◽  
Filtering Algorithm ◽  
Hybrid Filtering

scholarly journals Non-local mean filtering algorithm based on deep learning

2018 ◽  
Vol 232 ◽  
pp. 03025
Author(s):  
Baozhong Liu ◽  
Jianbin Liu
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Denoising Method ◽  
Filtering Algorithm ◽  
Local Mean ◽  
Non Local ◽  
Reduction Effect ◽  
Mean Filtering

Aimed at the problem that the traditional image denoising algorithm is not effective in noise reduction, a new image denoising method is proposed. The method combines deep learning and non-local mean filtering algorithms to denoise the noisy image to obtain better noise reduction effect. By comparing with Gaussian filtering algorithm, median filtering algorithm, bilateral filtering algorithm and early non-local mean filtering algorithm, the noise reduction effect of the new algorithm is better than the traditional method and the peak signal to noise ratio is compared with the early non-local mean algorithm. The performance is better.

Image Denoising Algorithm via Spatially Adaptive Bilateral Filtering

2013 ◽  
Vol 760-762 ◽  
pp. 1515-1518 ◽  
Author(s):  
Min Qi ◽  
Zuo Feng Zhou ◽  
Jing Liu ◽  
Jian Zhong Cao ◽  
Hao Wang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Image Denoising ◽  
Spatial Information ◽  
Bilateral Filtering ◽  
Denoising Method ◽  
Local Statistics ◽  
Filtering Algorithm ◽  
Spatially Adaptive ◽  
Intensity Information ◽  
Noise Experiment

The classical bilateral filtering algorithm is a non-linear and non-iterative image denoising method in spatial domain which utilizes the spatial information and the intensity information between a point and its neighbors to smooth the noisy images while preserving edges well. To further improve the image denoising performance, a spatially adaptive bilateral filtering image deonoising algorithm with low computational complexity is proposed. The proposed algorithm takes advantage of the local statistics characteristic of the image signal to better preserve the edges or textures while suppressing the noise. Experiment results show that the proposed image denoising algorithm achieves better performance than the classical bilateral filtering image denoising method.

Thenar Palmprint Image Denoising Based on Intermediary Filter

2013 ◽  
Vol 433-435 ◽  
pp. 338-341
Author(s):  
Yue Ming Dai ◽  
Xi Jun Zhu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Image Preprocessing ◽  
Filtering Algorithm ◽  
Truth Value ◽  
Noise Ratio ◽  
Value Measure ◽  
Objective Indicators

In this paper, based on the value measure of the Intermediary truth value, the Intermediary filtering algorithm is applied to the thenar palmprint image preprocessing. By using the Objective indicators of peak signal to noise ratio (PSNR), it can be seen that compared with the classical de-noising algorithm, the intermediary filtering algorithm is more effective and practicable.

scholarly journals HYBRID NOISE FILTERING ALGORITHM BASED ON NEURO-TYPE 2 FUZZY SYSTEMS

2017 ◽  
Vol 10 (13) ◽  
pp. 235
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Image Analysis ◽  
Data Acquisition ◽  
Image Denoising ◽  
Fuzzy Systems ◽  
Medical Images ◽  
Correct Diagnosis ◽  
Cost Effective ◽  
Noise Filtering ◽  
Filtering Algorithm

Medical images are often subjected to noise due to the failure of data acquisition hardware at the source. Thus, making it difficult for the radiologist toperform image analysis and give correct diagnosis of the disease. This research presents a new image denoising algorithm based on the combinationof neuro-type 2 fuzzy systems. The method not only preserves the information relevant for diagnostic details but also provides a cost-effective solutionfor recovery of lost information due to noise.

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