Salt n Pepper Image Noise Reduction

Noise reduction

Noise reduction is the process of removing noise from a signal. Noise reduction techniques are conceptually very similar regardless of the signal being processed, however a priori knowledge of the characteristics of an expected signal can mean the implementations of these techniques vary greatly depending on the type of signal.

All recording devices, both analogue and digital, have traits which make them susceptible to noise. Noise can be random or white noise with no coherence or coherent noise introduced by the devices mechanism or processing algorithms. In electronic recording devices, a major form of noise is hiss caused by random electrons that, heavily influenced by heat, stray from their designated path. These stray electrons influence the voltage of the output signal and thus create detectable noise.

In the case of photographic film and magnetic tape, noise (both visible and audible) is introduced due to the grain structure of the medium. In photographic film, the size of the grains in the film determines the film's sensitivity, more sensitive film having larger sized grains. In magnetic tape, the larger the grains of the magnetic particles (usually ferric oxide or magnetite), the more prone the medium is to noise.

Images taken with both digital cameras and conventional film cameras will pick up noise from a variety of sources. Many further uses of these images require that the noise will be (partially) removed - for aesthetic purposes as in artistic work or marketing, or for practical purposes such as computer vision.

Types

In salt and pepper noise (sparse light and dark disturbances), pixels in the image are very different in color or intensity from their surrounding pixels; the defining characteristic is that the value of a noisy pixel bears no relation to the color of surrounding pixels. Generally this type of noise will only affect a small number of image pixels. When viewed, the image contains dark and white dots, hence the term salt and pepper noise. Typical sources include flecks of dust inside the camera, or with digital cameras, faulty CCD elements.

Images with Salt and Pepper Noise

With 10% noise

With 75% noise

In Gaussian noise, each pixel in the image will be changed from its original value by a (usually) small amount. A histogram, a plot of the amount of distortion of a pixel value against the frequency with which it occurs, shows a normal distribution of noise. While other distributions are possible, the Gaussian (normal) distribution is usually a good model, due to the central limit theorem that says that the sum of different noises tends to approach a Gaussian distribution.

In either case, the noises at different pixels can be either correlated or uncorrelated; in many cases, noise values at different pixels are modelled as being independent and identically distributed, and hence uncorrelated. Most of these noise reduction techniques assume the presence of salt and pepper type of impulse noise. The detection of salt and pepper type of noise is relatively easy as there are only two intensity levels in the noisy pixels.

Median filter

In signal processing, it is often desirable to be able to perform some kind of noise reduction on an image or signal. The median filter is a nonlinear digital filtering technique, often used to remove noise. Such noise reduction is a typical pre-processing step to improve the results of later processing. Median filtering is very widely used in digital image processing because under certain conditions, it preserves edges whilst removing noise.

Median filtering is a nonlinear process useful in reducing impulsive or salt-and-pepper noise. It is also useful in preserving edges in an image while reducing random noise. Impulsive or salt-and pepper noise can occur due to a random bit error in a communication channel. In a median filter, a window slides along the image, and the median intensity value of the pixels within the window becomes the output intensity of the pixel being processed. For example, suppose the pixel values within a window are 5, 6, 55, 10 and 15, and the pixel being processed has a value of 55. The output of the median filter an the current pixel location is 10, which is the median of the five values.

Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images

A new median-based filter, progressive switching median (PSM) filter, is proposed to restore images corrupted by salt–pepper . A general framework of switching scheme-based image filters. impulse noise.

The algorithm is developed by the following two main points:

1) switching scheme—an impulse detection algorithm is used free image should be locally smoothly varying, and is separated by before filtering, thus only a proportion of all the pixels will be filtered edges [4]. The noise considered by our algorithm is only salt–pepper

2) progressive methods—both the impulse detection and the noise filtering procedures are progressively applied through several iterations. Simulation results demonstrate that the proposed algorithm is better than traditional median-based filters and is particularly effective for the cases where the images are very highly corrupted. impulsive noise which means:

1) only a proportion of all the image pixels are corrupted while other pixels are noise-free .

2) a noise pixel takes either a very large value as a positive impulse or a very small value as a negative impulse.

The progressive switching median (PSM) filter implements a noise detection algorithm before filtering. Both noise detection and filtering procedures are progressively repeated for a number of iterations

An Improved PSM Filter for Removing Impulse Noise

　
A new impulse-noise removing filter, which is composed of a improved PSM noise detector and a spline filter, is proposed. Using this filter, undetection of the noises can be avoided by controling the judgement value which is around maximum or minimum pixel value. In addition, it can also be possible to reduce mis-detection of the noises because the noise detecting threshold value can be set to a larger value. As a result of reducing both of undetection and mis-detection, proposed filtering method provides a good performance to remove impulsive noises. And the validity of the proposed method has been confirmed by computer simulations.

This algorithm sets a limit on the number of good pixels used in determine median and mean values, and substitute impulse pixel with half of the value of the summation of median and mean value. Experimental results show that the proposed algorithm performs a better noise filtering ability as the images are highly corrupted.