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

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.