Removal of Image Blurring and Mix Noises Using Gaussian Mixture and Variation Models

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Vipul Goel 1,* Krishna Raj 1

1. Dept. of Electronics Engineering, Harcourt Butler Technical University, Kanpur, 208002, India

* Corresponding author.


Received: 8 Sep. 2017 / Revised: 15 Sep. 2017 / Accepted: 22 Sep. 2017 / Published: 8 Jan. 2018

Index Terms

G-TV, GM-TV, Blurring and Noise


For the past recent decades, image denoising has been analyzed in many fields such as computer vision, statistical signal and image processing. It facilitates an appropriate base for the analysis of natural image models and signal separation algorithms. Moreover, it also turns into an essential part to the digital image acquiring systems to improve qualities of an image. These two directions are vital and will be examined in this work. Noise and Blurring of images are two degrading factors and when an image is corrupted with both blurring and mixed noises, de-noising and de-blurring of the image is very difficult. In this paper, Gauss-Total Variation model (G-TV model) and Gaussian Mixture-Total Variation Model (GM-TV Model) are discussed and results are presented. It is shown that blurring of the image is completely removed using G-TV model; however, image corrupted with blurring and mixed noise can be recovered with GM-TV model.

Cite This Paper

Vipul Goel, Krishna Raj," Removal of Image Blurring and Mix Noises Using Gaussian Mixture and Variation Models", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.1, pp. 47-55, 2018. DOI: 10.5815/ijigsp.2018.01.06


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