Version 1
: Received: 2 May 2020 / Approved: 5 May 2020 / Online: 5 May 2020 (02:36:28 CEST)
Version 2
: Received: 3 June 2020 / Approved: 4 June 2020 / Online: 4 June 2020 (05:52:55 CEST)
Bharati S., Khan T.Z., Podder P., Hung N.Q. (2021) A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications. In: Hassanien A.E., Khamparia A., Gupta D., Shankar K., Slowik A. (eds) Cognitive Internet of Medical Things for Smart Healthcare. Studies in Systems, Decision and Control, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-030-55833-8_3
Bharati S., Khan T.Z., Podder P., Hung N.Q. (2021) A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications. In: Hassanien A.E., Khamparia A., Gupta D., Shankar K., Slowik A. (eds) Cognitive Internet of Medical Things for Smart Healthcare. Studies in Systems, Decision and Control, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-030-55833-8_3
Bharati S., Khan T.Z., Podder P., Hung N.Q. (2021) A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications. In: Hassanien A.E., Khamparia A., Gupta D., Shankar K., Slowik A. (eds) Cognitive Internet of Medical Things for Smart Healthcare. Studies in Systems, Decision and Control, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-030-55833-8_3
Bharati S., Khan T.Z., Podder P., Hung N.Q. (2021) A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications. In: Hassanien A.E., Khamparia A., Gupta D., Shankar K., Slowik A. (eds) Cognitive Internet of Medical Things for Smart Healthcare. Studies in Systems, Decision and Control, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-030-55833-8_3
Abstract
Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.
Keywords
Gaussian noise; Speckle Noise; Mean square error(MSE); DE noising filters; Maximum difference value (MD); Peak signal to noise ratio(PSNR)
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Prajoy Podder
Commenter's Conflict of Interests: Author
2. Authors Information