Abstract
Denoising of medical images is an important pre-processing step for analysis, diagnosis, and treatment of various diseases. Images are normally affected by impulse noise when being transmitted through communication channels or because of noisy sensors. The most common noise that occurs in electronic communication is an impulse noise, specifically a salt-and-pepper noise. The median filter is typically used to reduce the presence of such noise. However, it works well for images with low-noise density. So, in order to get a better image restoration, we can use another image restoration technique which is adaptive median filtering which works very well for any density of noise. The adaptive median filter is frequently used in image processing to improve or restore data by eliminating undesirable noise without severely affecting the image’s structures. This method works in a two-step process. We tested the images containing noise levels ranging from 10 to 50% and calculated the PSNR value.
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Das, J.B.A., Sarangi, A., Mishra, D., Mohanty, M.N. (2022). Design of RAMF for Impulsive Noise Cancelation from Chest X-Ray Image. In: Mohanty, M.N., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 430. Springer, Singapore. https://doi.org/10.1007/978-981-19-0825-5_38
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DOI: https://doi.org/10.1007/978-981-19-0825-5_38
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