Median Filters 57r1w

A Spatial Median Filter for Noise Removal in Digital Images

Objective 

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The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. Comparing the different algorithm results.

Need of Noise Removal 

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The inexpensiveness and simplicity of point-and shoot cameras, combined with the speed at which budding photographers can send their photos over the Internet to be viewed by the world, makes digital photography a popular hobby. With each snap of a digital photograph, a signal is transmitted from a photon sensor to a memory chip embedded inside a camera. Transmission technology is prone to a degree of error, and noise is added to each photograph. Significant work has been done in both hardware and software to improve the signal-to-noise ratio in digital photography.

Literature Survey      

Mean Filter Median Filter Component Median Filter Vector Median Filter Spatial Median Filter Modified Spatial Median Filter

Modules   

Smoothing Algorithms Spatial Median Filter Modified Spatial Median Filter

Smoothing Algorithms 

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In software, a smoothing filter is used to remove noise from an image. Each pixel is represented by three scalar values representing the red, green, and blue chromatic intensities. At each pixel studied, a smoothing filter takes into the surrounding pixels to derive a more accurate version of this pixel. By taking neighboring pixels into consideration, extreme “noisy” pixels can be replaced. However, outlier pixels may represent uncorrupted fine details, which may be lost due to the smoothing process.

Block Diagram

Input Image

Random Noise Generation

Noisy Image

Impulse Detection

Output Image

Different types of Algorithms

Mean Filter 

The Mean Filter is a linear filter which uses a mask over each pixel in the signal.

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Each of the components of the pixels which fall under the mask are averaged together to form a single pixel.

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This new pixel is then used to replace the pixel in the signal studied.

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8+5+3+2+7+1+4+6+9 9

Mean=5

Median Filter 

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Drawback in the Mean Filter is poor at maintaining edges within the image. The Median Filter is performed by taking the magnitude of all of the vectors within a mask and sorting the magnitudes. The pixel with the median magnitude is then used to replace the pixel studied. The Simple Median Filter has an advantage over the Mean filter in that it relies on median of the data instead of the mean.

The median of a set is more robust with respect to the presence of noise.

How does Median Filter Work 

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First of all you have to sort the values of vectors and then you have to find out center value that is median value. If it will be matrix data means you have to sort the column values first and row values. A=[4 2 3;7 9 6;2 5 6]; 2 6 2 Median=5 A= 3 7 5 = 3 5 7 4 9 6

Component Median Filter 

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In the Simple Median Filter, each point in the signal is converted to a single magnitude. In the Component Median Filter each scalar component is treated independently. A filter mask is placed over a point in the signal. For each component of each point under the mask, a single median component is determined.

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These components are then combined to form a new point, which is then used to represent the point in the signal studied. When working with color images, however, this filter regularly outperforms the Simple Median Filter. When noise affects a point in a grayscale image, the result is called “salt and pepper” noise. In color images, this property of “salt and pepper” noise is typical of noise models where only one scalar value of a point is affected.

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For this noise model, the Component Median Filter is more accurate than the Simple Median Filter. The disadvantage of this filter is that it will create a new signal point that did not exist in the original signal, which may be undesirable in some applications.