Sorted Min-Max-Mean Filter for Removal of High Density Impulse Noise

This paper presents an improved Sorted-Min-Max-Mean Filter (SM3F) algorithm for detection and removal of impulse noise from highly corrupted image. This method uses a single algorithm for detection and removal of impulse noise. Identification of the corrupted pixels is performed by local extrema intensity in grayscale range and these corrupted pixels are removed from the image by applying SM3F operation. The uncorrupted pixels retain its value while corrupted pixel’s value will be changed by the mean value of noise-free pixels present within the selected window. Different images have been used to test the proposed method and it has been found better outcomes in terms of both quantitative measures and visual perception. For quantitative study of algorithm performance, Mean Square Error (MSE), Peak-Signal-to-Noise Ratio (PSNR) and image enhancement factor (IEF) have been used. Experimental observations show that the presented technique effectively removes high density impulse noise and also keeps the originality of pixel’s value. The performance of proposed filter is tested by varying noise density from 10% to 90% and it is observed that for impulse noise having 90% noise density, the maximum PSNR value of 30.03 dB has been achieved indicating better performance of the SM3F algorithm even at 90% noise level. The proposed filter is simple and can be used for grayscale as well as color images for image restoration

Exhaustive Study of Median filter

Image filtering plays an important role to remove impulse (Salt and Pepper) noise from the images. The median filter which is a non - linear filter is very effective at removing noise while preserving image features and edges. In comparison to linear filter, they provide excellent noise reduction capabilities, with less blurring. The work ing of median filter is by removing the corrupted pixel value with the median value of neighboring pixel which is calculated by sorting all the pixel values from the window into ascending order. This paper presents a median filtering algorithm by using 3* 3 windows

An Effective Noise Adaptive Median Filter for Removing High Density Impulse Noises in Color Images

Images are normally degraded by some form of impulse noises during the acquisition, transmission and storage in the physical media. Most of the real time applications usually require bright and clear images, hence distorted or degraded images need to be processed to enhance easy identification of image details and further works on the image. In this paper we have analyzed and tested the number of existing median filtering algorithms and their limitations. As a result we have proposed a new effective noise adaptive median filtering algorithm, which removes the impulse noises in the color images while preserving the image details and enhancing the image quality. The proposed method is a spatial domain approach and uses the 3×3 overlapping window to filter the signal based on the correct selection of neighborhood values to obtain the effective median per window. The performance of the proposed effective median filter has been evaluated using MATLAB, simulations on a both gray scale and color images that have been subjected to high density of corruption up to 90% with impulse noises. The results expose the effectiveness of our proposed algorithm when compared with the quantitative image metrics such as PSNR, MSE, RMSE, IEF, Time and SSIM of existing standard and adaptive median filtering algorithms

Effective Approach for Extracting Noise from Digital Image and Real Time Data using Filtering Technique

Digital image are made out of pixels and we know pixel is the smallest component of a picture. Every pixel speaks to the dark level for highly contrasting photographs at a solitary point in the image, so a pixel can be spoken to by a small speck of particular shading. In an image pixel having intensity in range of 0-255 and if pixel having intensity zero it means black and having intensity 255 it means white and in between them then considered as gray level. There are various types of images and various types of noises occurred and to remove them diverse filters are available and every filter are having own advantage and disadvantages and suitable for a particular types of noised which it can remove efficiently. In our research work our main target is to fetch out salt and pepper noise. In base paper at two levels of S&P noise filter is used to denoise the image to get various parameters. But in our research work salt and pepper noise at various levels targeted and removed efficiently with parameters PSNR, MSE and IEF. Noise is random in nature and it can be mixed with image anywhere therefore diverse noise models were studied deeply. Restoration efficiency was checked by PSNR and mean square error (MSE) into considerations