Abstract

 Removal of Noise in the image is challenging task in Image processing. Now-a-days there are so many methods that are available to remove noise from digital images. This introduced noise by image enhancement methods, in particular algorithms based on the random spray sampling technique, but not only. According to the nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. Most of the novel method comprises two stages: the first stage is to detect the noise in the image. At this stage, based on the intensity values, the pixels are roughly divided into “noise-free pixel” and “noisy pixel”. Then, the second stage is to eliminate the noise from the image. At this stage, only the “noise-pixels” are processed. Some methods proposed to remove noise in the Image based on Contourlet-Domain HMT Models Using Cycle Spinning, Spatio-Temporal Retinex inspired Envelope with Stochastic Sampling (STRESS) used in gray scale images, steerable pyramid domain based on a local Laplace prior, Dual-tree Complex Wavelet Transform based denoising for Random Spray image enhancement methods, Adaptive Shrinkage for Image Denoising Based on Contourlet Transform. But these are some disadvantages in them.

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