Comparison of Contourlet and Time-Invariant Contourlet Transform Performance for Different Types of Noises

Year 2019, Volume: 7 Issue: 4, 399 - 404, 30.10.2019

Muhammet Fatih Aslan , Kadir Sabancı , Akif Durdu

https://doi.org/10.17694/bajece.573583

Cited By: 1

Abstract

A noiseless image is
desirable for many applications. However, this is not possible. Generally, wavelet-based
methods are used to noise reduction. However, due to insufficient performance
of wavelet transforms (WT) on images, different multi-resolution analysis
methods have been proposed. In this study, one of them is Contourlet Transform
(CT) and the Translation-Invariant Contourlet Transform (TICT) which is an
improved version of CT is compared using different noises. The fundus images
are taken from the DRIVE dataset and benchmark images are used. Peak
Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Structural
Similarity (MSSIM) and Feature Similarity Index (FSIM) are used as comparison
criteria. The results showed that TICT is better in Gaussian noisy images.

Keywords

Contourlet Transform, Image Denoising, Time Invariant Contourlet Transform

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