IMAGE DENOISING USING WAVELET AND SHEARLET TRANSFORM

Bharath Kumar S; Kavyashree; Ananth V Naik; Kavyashree C.L; Gayathri K.M

doi:10.29121/granthaalayah.v5.i4RACEEE.2017.3316

Authors

Bharath Kumar S UG Students,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain University, Bangalore-562112, India
Kavyashree S UG Students,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain University, Bangalore-562112, India
Ananth V Naik UG Students,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain University, Bangalore-562112, India
Kavyashree C.L UG Students,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain University, Bangalore-562112, India
Gayathri K.M Assistant Professor,Department of Electronics and Communication Engineering, School of Engineering and Technology, Jain University, Bangalore-562112, India

DOI:

https://doi.org/10.29121/granthaalayah.v5.i4RACEEE.2017.3316

Keywords:

Types of Noise, Denoising Filters, Histogram, Wavelet Transform, ShearletTransform, MATLAB 2016b

Abstract [English]

Image plays an important role in this present technological world which further leads to progress in multimedia communication, various research field related to image processing, etc. The images are corrupted due to various noises which occur in nature and poor performance of electronic devices. The various types of noise patterns observed in the image are Gaussian, salt and pepper, speckle etc. due to which the image is attenuated or amplified. The main challenge lies in removing these noises. We use various denoising techniques in removal of noise in order to retrieve the original information from the image. Wavelet transforms are one of the denoising algorithms used as conventional methods. This algorithm is used to capture the image along different directions in limited manner which becomes the main disadvantage of using this algorithm. In this work we propose a technique by integrating Wavelet and Shearlet transform which effectively removes the noise to the maximum extent and restores the image by edge detection which can be identified. The simulation is done on synthetic image and shows improvement with existing methods. The algorithm is simulated in MATLAB 2016b.

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