An Improved Approach for Removal of Salt and Pepper Noise in MR Images

Author(s):  
Monika Jyotiyana ◽  
Nishtha Kesswani ◽  
Anupam Agarwal
2019 ◽  
Vol 118 (7) ◽  
pp. 73-76
Author(s):  
Sharanabasappa ◽  
P Ravibabu

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter  in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.


2013 ◽  
Vol 32 (5) ◽  
pp. 1293-1295
Author(s):  
Yuan-hua GUO ◽  
Xiao-rong HOU

2012 ◽  
Vol 31 (3) ◽  
pp. 185 ◽  
Author(s):  
Rajamani Arumugham ◽  
Krishnaveni Vellingiri ◽  
Wassim Ferose Habeebrakuman ◽  
Kalaikamal Mohan

In this paper a novel Lone Diagonal Sorting (LDS) algorithm for denoising color images and videos co-rrupted with salt and pepper noise is proposed. The proposed lone diagonal sorting algorithm uses diagonal sorting alone for denoising of impulse noise. The algorithm has been implemented and tested for various color images and video signals and appreciable performance in terms of PSNR, MSE and SSIM is obtained. Our algorithm has been compared with other standard algorithms. A drastic improvement in the computational time has been achieved without compromising much on the visual quality after reconstruction.


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