Introduction

 

Magnifying glass in the image

 

View more images

 

Geometrical transforms

 

Brightness profile

 

Histogram

 

Fourier transform

 

Features

 

Brightness/contrast corrrections

 

Flat-Field corrections

 

Inhomogeneous lighting correction

 

Gama correction

 

Noise generation

 

Arithmetical and logical operation

 

Look Up Tables

 »

2D convolution

 

Objects drawing

 

Morphological operations

 

Nonlinear filters

 

Bit fields

 

Sobel edge detection

 

Averaging

 

Colour images

 

 MIPS 2.0 - Medical/Microscopy Image Processing Software

 

 

2D CONVOLUTION


Image / Convolution

According to their purpose, convolution operations can be divided into two groups - image smoothing (noise suppression) and gradient operations (edge enhancement). The equation below describes a two-dimensional discrete convolution with a convolution kernel a. This convolution kernel is sometimes referred to as a convolution mask, and in filter theory as the impulse response of the filter. The appropriate selection of the convolution kernel is usually based on experiments or knowledge of the frequency properties of the image signal.


                                           Image filtering based on 2D convolution 

Preview

 

 

 

 

 

 

 

 

 

Example

 

 

          Original image (dimension 320 x 240)

1. Gradient kernels

 

 

                       Derivate North 1

 

 

 

 

                        Derivate East 1

 

 

 

 

                      Derivate South 1

 

 

 

 

                        Derivate West 1

2. Edge detection kernels

 

 

                    Horizontal line detect

 

 

 

 

                       Vertical line detect

 

 

 

 

                           1st Diagonal

 

 

 

 

                           Laplacian 3

3. Efect kernels

 

 

                     Blurring 5 x 5

 

 

 

 

                 Enhancement - strong

4. Mathematical kernels

 

 

                     Shift Edge Elements

 

 

 

 

                          Compass Max

 

 

 

 

                          Kirsch Max

 

 

 

 

                       Max Abs  9 x 9

 

 

 

 

                 Roberts Mean Square

 

 

 

 

                    Sobel Mean Square

5. Filter kernels

 

 

                          Low Pass

 

 

 

 

                            High Pass

6. Newly designed kernel

 

                1/18 .

20

1

1

1

20

1

-8

-8

-8

1

1

-8

-10

-8

1

1

-8

-8

-8

1

20

1

1

1

20

 

                      Image after convolution                                                          Used kernel

 

 


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