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

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 insert ignore into journalissuearticles values(WT); on images, different multi-resolution analysis methods have been proposed. In this study, one of them is Contourlet Transform insert ignore into journalissuearticles values(CT); and the Translation-Invariant Contourlet Transform insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(PSNR);, Mean Squared Error insert ignore into journalissuearticles values(MSE);, Mean Structural Similarity insert ignore into journalissuearticles values(MSSIM); and Feature Similarity Index insert ignore into journalissuearticles values(FSIM); are used as comparison criteria. The results showed that TICT is better in Gaussian noisy images.