COMPARATIVE STUDY OF ALGORITHMS/TECHNIQUES FOR DENOISING OF GAUSSIAN NOISE

VIPUL SINGH

Abstract


Noise is a random variation of image intensity and visible as grains in the image.Gaussian noise is one of the noise that can be found in gray as well as colored images. Gaussian noise is a noise that has a random and normal distribution of instantaneous amplitudes over time. Gaussian noise is statistical noise having a probability density function equal to that of the normal distribution, which is also known as the Gaussian distribution. A lot of algorithms and techniques have ben developed to remove the gaussian noise from the image (both gray scale and colored).In this paper, we compare the Bilateral Filter, Block-matching and 3D filtering , Gaussian smoothing Filter, Median Filter and Spatial gradient Bilateral Filter for gray scale images and, Adaptive Bilateral Filter and Sparse 3-D transform-domain collaborative Filter for colored images. A comparative study based on Peak Signal to Noise Ratio and Mean Absolute Error of these algorithms has been provided in this paper.

Keywords


Noise is a random variation of image intensity and visible as grains in the image.Gaussian noise is one of the noise that can be found in gray as well as colored images. Gaussian noise is a noise that has a random and normal distribution of instantaneous

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References


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Vipul Singh received the Bachelor of Technology(Hons) degree in Information

Technology from J.S.S, Noida (Uttar Pradesh) in 2016. His area of interest includes Speech Signal Processing,

Digital Image Processing, Machine Learning and Artificial Intelligence.

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DOI: https://doi.org/10.26483/ijarcs.v8i8.4614

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