1. Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5

2. Hema ENT 273 Lecture 5 2 Why Filters are needed? Image processing converts an input image into an enhanced image from which information about the image can be retrieved. To enhance images any unwanted information or distortions called noise has to be removed. Filtering is the process which removes noise from an image [which also includes lightening darker regions to enhance quality of the image or suppresses unwanted information /region] Original Image Image with Noise

3. Hema ENT 273 Lecture 5 3 Image Noise Images are formed by light falling on a sensor Noises are introduced due to –Quantization – which reduces the light levels to 256 –Imperfect sensors –Imperfect lighting conditions during acquisition –Compression formats

4. Hema ENT 273 Lecture 5 4 Types of Noise Salt and Pepper –In salt and pepper noise pixels in the image are vastly different in color from their surrounding pixels. –The color 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.. Gaussian Noise –In Gaussian noise (dependent noise), an amount of noise is added to every part of the picture. – Each pixel in the image will be changed from its original value by a (usually) small amount. –Taking a plot of the amount of distortion of a pixel against the frequency with which it occurs produces a Gaussian distribution of noise Uniform Noise –Pixel values are usually close to their true values. –Average value is equal to the real one Original Image Salt and Pepper Noise Gaussian Noise Uniform Noise

5. Hema ENT 273 Lecture 5 5 Noise Removal Most Noise removal processes are called filters –Applied to each point in an image [convolution] –Use information in the small local windows of a pixel Noise removal Filters –Linear Filters –Non-Linear Filters Linear Filters –Gaussian filters –Mean Filter Non-Linear Filters –Median Filter

6. Hema ENT 273 Lecture 5 6 Linear Systems Space Invariant System –A system whose response remains the same irrespective of the position of the input pulse Output impulse response Linear Space Invariant System Input impulse

7. Hema ENT 273 Lecture 5 7 Linear Space Invariant Systems Linear Space Invariant System g(x,y) Input Image Output Image In LSI systems the output h(x,y) is a convolution of f(x,y) with impulse response g(x,y)

8. Hema ENT 273 Lecture 5 8 P1P2P3 P4P5P6 P7P8P9 h [i,j] ABC DEF GHI Convolution Mask

9. Hema ENT 273 Lecture 5 9 Convolution –A neighborhood operation in which each output pixel is a weighted sum of neighboring input pixels. The weights are defined by the convolution kernel. Image processing operations implemented with convolution include smoothing, sharpening, and edge enhancement. –Convolution is a spatially invariant operation

10. Hema ENT 273 Lecture 5 10 Linear Filters

11. Hema ENT 273 Lecture 5 11 Gaussian Filter Gaussian filters removes noise by smoothing but also blurs the image, The degree of smoothing is determined by the standard deviation of the Gaussian. (Larger standard deviation Gaussians, of course, require larger convolution masks in order to be accurately represented.) The Gaussian outputs a `weighted average' of each pixel's neighborhood, with the average weighted more towards the value of the central pixels. This is in contrast to the mean filter's uniformly weighted average. Gaussian provides gentler smoothing and preserves edges better than a similarly sized mean filter - Standard deviation of the distribution

12. Hema ENT 273 Lecture 5 12 Mean Filter The mean filter is a simple sliding-window spatial filter that replaces the center value in the window with the average (mean) of all the pixel values in the window. The window, or kernel, is usually square but can be any shape. An example of mean filtering of a single 3x3 window of values is shown 537 264 819 *** *5* ***

13. Hema ENT 273 Lecture 5 13 Median Filter The median filter is also a sliding-window spatial filter, but it replaces the center value in the window with the median of all the pixel values in the window. As for the mean filter, the kernel is usually square but can be any shape. An example of median filtering of a single 3x3 window of values is shown Median filter remove 'impulse' noise (outlying values, either high or low). The median filter is also widely claimed to be 'edge-preserving' since it theoretically preserves step edges without blurring. However, in the presence of noise it does blur edges in images slightly. 3577 256 1849 *** *6* *** 2,3,4,5,6,7,9,18,57

14. Hema ENT 273 Lecture 5 14 Histogram Modification Histogram equalization –Is a method for stretching contrast of unevenly distributed gray values by uniformly redistributing the gray values

15. Hema ENT 273 Lecture 5 15 References Computer Vision – Linda G Shapiro & George Stockman http://en.wikipedia.org/wiki/Image_noise Mat lab reference notes

16. Machine Vision End of Lecture 5