Computer Science‎ > ‎

### Intro to Digital Image Processing (Basic filters and Matlab examples.)

Image processing tutorial -- Basic Kernel-based Filters – By Deepan Gupta

A Quick Summary and outline of topics introduced in the tutorial

In the following tutorial we will analyse basic kernel based filters used in image processing and understand their usability. We will test these filters on the images and study the outputs. Kernel-based Filters Kernel basically refers to a filter function applied to the input in order to obtain the filtered output. For different kind of filters we have different filters. In case of images filtering can of various types e.g. Noise removal, Boundary Detection, Averaging, Contrast Stretching etc. As we know that Images are represented in a form of 2-Dimensional Matrix. Therefore the filtering operation mainly uses convolution of input image with a small mask (matrix) which is called as Kernel. The output of the filtering operation also depends on the size of the kernel.

Band-Pass Filters: Band Pass Filters allow certain range of frequencies to pass through. They are used for various Image enhancement operations like edge enhancement, sharpening. High pass filter and Low pass filters are the examples of band-pass filters.
Low-Pass Filter: Low pass filter is one of the most basic kernel based filters. It is called so because it passes the lower frequencies ie, it passes the lower contrasting regions. In other words, it focuses on the homogeneous regions.
High-Pass Filter: High-pass filters are just the opposite of Low-pass filter. They allow high frequncies to pass. Only regions having high rate of change in pixel values are retained. It is used for Boundary detection.
High-Boost Filter: High Boost filter is used for sharpening of the images. In high-boost filter we enhance the high frequencies regions output the enhanced image.
Sobel Filter: Sobel Filter is one of the most commonly used filters for edge detection. The results obtained are very accurate and can be manipulated according to various images detailing. We make use of two kernel masks and obtain two resultant images. Then final output is obtained with the help of both the images.

The Tutorial document is provided here. After this document, we also provide you with the Matlab Scripts for running some of the filters discussed.

### High Pass Filter in Matlab

function[] = HighBoost(InputImageName, A, maskSize, outputImageName, IntermediateImageName)
InputImageSize = size(InputImageMatrix);
OutputImageMatrix = uint8(zeros(InputImageSize));

for InputImageRow = 1:InputImageSize(1)
for InputImageColumn = 1:InputImageSize(2)
temp = double(InputImageMatrix(InputImageRow, InputImageColumn)) * A - double(InputImageMatrixl(InputImageRow, InputImageColumn));
if (temp < 0)
OutputImageMatrix(InputImageRow, InputImageColumn) = 0;
elseif (temp > 255)
OutputImageMatrix(InputImageRow, InputImageColumn) = 255;
else
OutputImageMatrix(InputImageRow, InputImageColumn) = uint8(temp);
end
end
end
imwrite(OutputImageMatrix, outputImageName);
end

### Sobel Filter in Matlab

`function []=Sobel(InputImageName,OutputImageName,Threshold)`
`    imwrite(edge(imread(InputImageName),'sobel',Threshold),OutputImageName);`
`end`

High Pass Filter in Matlab

`function[]=HighBoost(InputImageName,maskSize,outputImageName,IntermediateImageName)`
`HighBoost(InputImageName,1,maskSize,outputImageName,IntermediateImageName)`
`end`