Noise Estimation using Kalman Filter in Image Restoration
B. Nagasirisha1, V.V.K.D.V. Prasad2
1B.Nagasirisha, Department of ECE, Gudlavalleru Engineering College, Gudlavalleru (Andhra Pradesh), India.
2V.V.K.D.V. Prasad, Department of ECE, Gudlavalleru Engineering College, Gudlavalleru (Andhra Pradesh), India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 24 September 2019 | Manuscript Published on 10 October 2019 | PP: 888-891 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F12170886S219/19©BEIESP | DOI: 10.35940/ijeat.F1217.0886S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Speckle Noise, Salt and Pepper Noise, Kalman Filter.
Scope of the Article: Image Security