Table I.

Analysis of denoising filtering techniques.

Analysis of Denoising Filtering Techniques	Features	Advantages	Disadvantages
Homomorphic Wavelet [3]	Threshold can be extended that gives better result	Reduce speckle noise	Complex technique
Soft Thresholding [4]	“Optimal recover model and Statistical inference”	Reduce as well as smooth the noise	Large threshold cuts the coefficients
Non Homomorphic [5]	Relies on characterization of the marginal statics of the signal and speckle wavelet coefficients	Reduce the computational complexity of filtering method	Not a robust method for estimation distribution parameters
Adaptive wavelet domain Bayesian processor [6]	Combines the MAP estimation with correlated speckle noise	Speckle noise suppressed and remaining structure of image is not effected	Not effective technique
Wavelet based statistical [7]	Use realistic distribution of wavelet coefficients	Feature preserve, better for medical images, fast computation	Highly complex
Versatile technique for visual enhancement [8]	Combining MAP and speckle and signal wavelet coefficients	High correlation and structure similar and quality index	Cover only medical images not other
Wavelet thresholding (normal shrink) [9]	Sub band adaptive threshold	Normal shrink is faster as compare to bayes shrink	Need to reduce the number of bits while using normal shrink
Joint optimization quantization and wavelet packets (JTQ-WP) [10]	Covers both us images and natural images	Highly compressed approach	Cost function is high
Curvelet and contourlet [11]	Noise improvement rectangle	High PSNR can be achieved	Consider only Gaussian noise not other noises