Mean Filter

• It is also called as Box Averaging filtering technique. 
• It uses a kernel and is based on convolution.
• It calculates the average of all a pixel and its surrounding pixels and the result is assigned to the central pixel.
• It is a very effective technique for the removal of Poisson noise.

Syntax:

 k=rgb2gray(k); // To convert into grayscale: 

 gaussian_noise = imnoise(k, ‘gaussian’, 0, 0.01);

// To create the image corrupted with gaussian noise:

denoised=conv2(noisy_image, mean_filter, ‘same’); 

// To perform the convolution between gaussian_noisy image and mean filter:

To display the image: imtool( image_variable, [ ]);

To create the image corrupted with poisson noise: poisson_noise = imnoise(k, ‘poisson’);

 salt_noise = imnoise(k, ‘salt & pepper’, 0.05);

// To create the image corrupted with salt and pepper noise:

 speckle_noise = imnoise(k, ‘speckle’, 0.05);

// To corrupt the image with speckle noise:

Example:

Output:

Mean filter does not remove any particular noise effectively. 

Code Explanation: 

• k=imread(“einstein_colored); This line reads the image.
• k=rgb2gray(k); this line converts into grayscale.
• gaussian_noise = imnoise(k, ‘gaussian’, 0, 0.01); this line creates the image corrupted with gaussian noise.
• gaussian_denoised=conv2(gaussian_noise, h, ‘same’); this line performs the convolution between gaussian_noisy image and mean filter.
• poisson_noise = imnoise(k, ‘poisson’); this line creates the image corrupted with poisson noise.
• poisson_denoised=conv2(poisson_noise, h, ‘same’); this line performs the convolution between poisson_noise image and mean filter.
• salt_noise = imnoise(k, ‘salt & pepper’, 0.05); this line creates the image corrupted with salt and pepper noise with 5 % of total pixels.
• salt_denoised=conv2(salt_noise, h, ‘same’); this line performs the convolution between salt_noise image and mean filter.
• speckle_noise = imnoise(k, ‘speckle’, 0.05); this line corrupts the image with speckle noise of 0.05 variance.
• speckle_denoised=conv2(speckle_noise, h, ‘same’); this line performs the convolution between speckle_noise image and mean filter.
• imtool(k, []); this line displays the original image.
• imtool(noised_image, []); this line displays the noisy_image.
• imtool(denoised_image, []); this line displays the denoised image.

Digital images are prone to various types of noise that make the quality of the images worst. Image noise is a random variation of brightness or color information in the captured image. Noise is basically the degradation in image signal caused by external sources such as cameras. Images containing multiplicative noise have the characteristic that the brighter the area the noisier it. We shall discuss various denoising filters in order to remove these noises from the digital images. 

Types of filters discussed in this article are listed as:

• Mean filter
• Median filter
• Gaussian filter  
• Wiener filter