Performance Analysis of Impulse Noise Reduction Algorithms: Survey

Impulse noise is corrupted in the field of DIP applications of transmission and reception due to unwanted disturbance. So, the original image will corrupted at the acquiring end. The various denoising filter algorithm of impulse noise reduction in this survey were studied. So the impulse noise detection and removal technique, they have several drawbacks also discussed for various image with various denoising filter methods and also comparative results study with psnr values and reconstructed image in this survey. KeywordsImpulse noise detector, noise reduction, Power consumption, Edges details and Image quality. Introduction The basic idea to behind in this research is the restoration of original image from the distorted image corrupted by impulse noise. It is also referred to as image “denoising”. Denoising is the process of removing unwanted noise from an image. A denoised image is an approximation to the underlying true image, before it was contaminated. Image denoising finds applications in fields such as astronomy, medical imaging and forensic science. Different types of noise frequently contaminate images. Impulse noise is one such noise, which is frequently introduced into images while transmitting and acquiring them due to channel errors or in storage media due to faulty hardware. According to the distribution of noisy pixel values, impulse noise can be classified into two categories: Fixed-Valued impulse noise and Random-Valued impulse noise. The Fixed Values impulse noise is also known as “Salt and Pepper Noise” since the pixel value of a noisy pixel is either minimum or maximum value in grayscale images. The values of noisy International Journal of Bio Sciences and Engineering, Vol 1(1), 50-63, August 2014 ISSN2349 5200 www.ijbse.in Page 51 pixels corrupted by random valued impulse noise are uniformly distributed in the range of [0, 255] for gray-scale images. Removal of Random valued impulse noise is more complicated due to the random distribution of the noise pixels. In this paper, the main focus is on the detection and correction of the random-valued impulse noise from the corrupted image. Recently many methods have been proposed for the removal of Impulse noise from the image. Many methods recently have been proposed for the removal of Impulse noise from the image. Some of them employ the standard median filter or its modifications. However, these approaches might blur the image since both noisy and noise-free pixels are modified. To avoid the damage on noise-free pixels, an efficient switching strategy has been proposed in the related works. In general, the switching median filter (Non-linear technique) consists of two steps: 1) impulse detection and 2) noise filtering. It locates the noisy pixels with an impulse detector, and then filters them rather than the whole pixels of an image to avoid causing the damage on noise-free pixels. These filter work with low noise ratios, and are very poor when the noise ratio reaches above 40%. In this survey, a various denoising algorithm performs preserves the image details effectively than other older technique. The design uses two steps for easy computation—impulse detection and mean filtering. The mean filtering does not affect the edges or other small structures in the image. This method is more important for the restoring of corrupted images. After the impulse noise is detected, only those pixels are processed by the filter algorithm and reconstructed the corrupted image in good manner as shown in Fig.1.