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.

scholarly journals Overview of image noise reduction based on non-local mean algorithm

2018 ◽  
Vol 232 ◽  
pp. 03029 ◽  
Author(s):  
Baozhong LIU ◽  
Jianbin LIU
Keyword(s):  
Noise Reduction ◽  
Development Process ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Mean Square ◽  
Signal To Noise ◽  
Image Noise Reduction ◽  
Local Mean ◽  
Non Local ◽  
Noise Reduction Algorithm

The system introduces the extensive application and development process of image denoising based on non-local mean. The principle and specific theoretical model of the non-local mean algorithm are described. The process of improving the non-local mean algorithm after being proposed and how to improve it is elaborated and the shortcomings of the algorithm are pointed out. The noise reduction algorithm is experimentally described in detail from the aspects of peak signal-to-noise ratio, mean square error and structural similarity under different noise environment conditions.

scholarly journals A Non-Local Low-Rank Algorithm for Sub-Bottom Profile Sonar Image Denoising

2020 ◽  
Vol 12 (14) ◽  
pp. 2336 ◽  
Author(s):  
Shaobo Li ◽  
Jianhu Zhao ◽  
Hongmei Zhang ◽  
Zijun Bi ◽  
Siheng Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Low Rank ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
Non Local ◽  
Better Than ◽  
Total Average

Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.

Threshold Noise Reduction Research of Improved EEMD Method

2012 ◽  
Vol 226-228 ◽  
pp. 237-240 ◽  
Author(s):  
Mei Jun Zhang ◽  
Hao Chen ◽  
Chuang Wang ◽  
Qing Cao
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Time Frequency ◽  
Noise Ratio ◽  
Reduction Effect ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

scholarly journals Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image

2016 ◽  
Vol 195 ◽  
pp. 88-95 ◽  
Author(s):  
Jian Yang ◽  
Jingfan Fan ◽  
Danni Ai ◽  
Xuehu Wang ◽  
Yongchang Zheng ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Medical Ultrasound ◽  
Local Statistics ◽  
Mean Filter ◽  
Local Mean ◽  
Non Local ◽  
Speckle Noise Reduction

scholarly journals IMPLEMENTATION OF MODIFIED MEDIAN FILTERING ALGORITHM FOR SALT & PEPPER NOISE REDUCTION ON IMAGE

2017 ◽  
Author(s):  
Robbi Rahim ◽  
Ali Ikhwan
Keyword(s):  
Image Quality ◽  
Noise Reduction ◽  
Special Technique ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Analog Signals ◽  
Pixel Value ◽  
The Mean ◽  
Spatial Window ◽  
Mean Filtering

Noise is one form of issue in the image, salt & pepper noise is the kind of noise that can be made using a special technique or also due to the conversion from analog signals to digital, the noise can be improved by using algorithms such as the mean filtering, the mid-point filtering and median filtering, median filtering algorithm is widely used for repair image quality, this article will discuss the modification of the median filtering to improve noise in the image by taking the average of neighboring pixels by 2 points from the value of the center clockwise, the value is taken to be processed to retrieve the value of the middle and then the overall result value will be divided to replace the center pixel value 3x3 spatial window.

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