Robust modeling for image restoration using a modified reduced update Kalman filter

1992 ◽  
Vol 40 (10) ◽  
pp. 2584-2588 ◽  
Author(s):  
H.B. Belaifa ◽  
H.M. Schwartz
Keyword(s):  
Kalman Filter ◽  
Image Restoration ◽  
Robust Modeling

scholarly journals Noise Estimation using Kalman Filter in Image Restoration

2019 ◽  
Vol 8 (6S2) ◽  
pp. 888-891
Keyword(s):  
Image Processing ◽  
Kalman Filter ◽  
Image Restoration ◽  
Medical Image ◽  
Blind Deconvolution ◽  
Medical Images ◽  
Medical Image Processing ◽  
Noise Removal ◽  
Original Image ◽  
Degraded Image

Medical image processing plays a vital role in medical sciences from the past decades. Medical image processing becomes simple and useful with the advancement of image processing techniques. Medical images are used to observe the information related to inside the organs of human body. For better diagnoses and analysis of disease the image should be clear, noise free and more informative also. Usually medical images are corrupted by different noises in image acquisition and transmission process. The basic challenge in medical image processing is noise removal without losing diagnostic information. Image restoration is the one of the technique to recover the original image from the degraded image. In this paper, we are proposing a kalman filter to estimate the noise function from the degraded image and to reconstruct the original image. Here we are taking into account that the medical image was corrupted by the gaussian, speckle and salt & pepper noise. The simulation result infers that the proposed blind deconvolution method can be able to suppress the noise well and also preserve edge information without losing diagnostic data.

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