Table 4.

Review of medical denoising approaches.

Title, Author, Publication, Year	Classes	Tools/Techniques	Advantages
Title: “Homomorphic wavelet thresholding technique for denoising medical ultrasound images” [3] Authors: “S. Gupta, R. C. Chauhan and S. C. Saxena” Publication: “Journal of Medical Engineering & Technology (2005)”	Image denoising	Novel Homomorphic Wavelet Thresholding	It outperform the most effective wavelet based denoising
Title: “De-Noising by Soft-Thresholding” [4] Authors: “David L. Donoho” Publication: “IEEE Transaction On Information Theory (1995)”	Image denoising	Abstract De-Noising Model	Increases statistical inference
Title: “Robust non-homomorphic approach for speckle reduction in medical ultrasound images” [5] Authors: “S. Gupta, R.C. Chauhan and S.C. Saxena” Publication: “Medical & Biological Engineering & Computing (2005)”	Speckle reduction	Non-Homomorphic technique	Low complexity
Title: “Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modeling based on Rayleigh distribution” [6] Authors: “S. Gupta, R.C. Chauhan and S.C. Saxena” Publications: “IEEE(2005)”	Speckle reduction	Discrete Wavelet Transform, MAP estimator	Suppresses speckle noise effectively
Title: “A Wavelet Based Statistical Approach for, Speckle Reduction in Medical Ultrasound Images” [7] Authors: “Savita Gupta, L. Kaur, R.C. Chauhan and S. C. Saxena” Publication: “IEEE (2003)”	Speckle reduction	Novel Multiscale Nonlinear for Speckle Reduction	Fast computation and better diagnosis
Title: “A versatile technique for visual enhancement of medical ultrasound images” [8] Authors: “L. Kaur, S. Gupta, R.C. Chauhan, S.C. Saxena” Publication: “Science direct (2007)”	Visual enhancement of image	Versatile Wavelet Domain despeckling	Provide better performance in speckle smoothing and edge preservation
Title: “Wavelet-based statistical approach for speckle reduction in medical ultrasound images” [9] Authors: “S. Gupta, R.C. Chauhan and S.C. Saxena” Publication: “Medical & Biological Engineering & Computing (2004)”	Speckle reduction	Novel Speckle-Reduction	Fast computation and Despeckling
Title, Author, Publication, Year	Classes	Tools/Techniques	Advantages
Title: “Medical ultrasound image compression using joint optimization of thresholding quantization and best-basis selection of wavelet packets” [10] Authors: “L. Kaur, S. Gupta, R.C. Chauhan, S.C. Saxena” Publication: “Science direct (2007)”	Image denoising	Image Coding Algorithm	Performance of JTQ-WP coder is concluding better
Title: “Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising” [11] Authors: “Vipin Milind Kamble, Pallavi Parlewar, Avinash G. Keskar, Kishor M. Bhurchandi” Publication: “Springer (2015)”	Image denoising	X’let transform	Provide effective denoising
Title: “Denoising Of Medical Ultrasound Images In Wavelet Domain” Authors: “Amit Jain” [14] Publication: “International Journal Of Engineering And Computer Science (2015)”	Image denoising	Wavelet Transformation, Wavelet Thresholding	Preserves image and visual quality
Title: “Image Denoising using Wavelet Thresholding” [15] Authors: “LakhwinderKaur, Savita Gupta and R.C. Chauhan (2002)”	Image denoising	Adaptive Threshold Estimation	Provide smoothness and Effective edge preservation
Title: “Image denoising using curvelet transform: an approach for edge preservation” [16] Authors: “Anil A. Patil and Jyoti Shinghai” Publication: “Journal of scientific and industrial research (2010)”	Image denoising	Soft Thresholding Multiresolution	Improve smoothness
Title: “ Ideal spatial adaptation by wavelet shrinkage” [17] Authors: “David l. donoho and iain m. johnstone” Publication: “ Biometrika (1994)”	Speckle reduction	Signal-dependent Multiplicative Speckle Noise Model, Discrete Wavelet Transform and Modeling of Wavelet Coefficients	Smoothness increases