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Image processing SaltPepper Noise
This document discusses image noise reduction systems. It defines two main types of images - vector images defined by control points and digital images defined as 2D arrays of pixels. It describes different types of digital images like binary, grayscale, and color images. It then discusses image noise sources, types of noise like salt and pepper, Gaussian, speckle and periodic noise. Various noise filtering techniques are presented like minimum, maximum, mean, median and rank order filtering to remove salt and pepper noise.
In this document
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Introduction of an image noise reduction system.
Discussion on image types: Vector and Digital images; details on Binary, Gray scale, and Color images. Details on digital images as 2-D arrays, pixel definitions, and classifications of image types.
Definition of image noise arising from transmission errors and various factors affecting it.
Overview of different types of image noise: Salt and Pepper, Gaussian, Speckle, and Periodic noise.
Illustration of pixel neighbors within a specified 3x3 region.
Various filtering techniques for salt and pepper noise removal: Minimum, Maximum, Mean, Rank Order, Median, and New Generated Filtering.
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Image processing SaltPepper Noise
- 1. IMAGE NOISE REDUCTIONSYSTEM
- 2. IMAGES • There aretwo types of images • Vector Images • Digital Images
- 3. VECTOR IMAGES • Vectorimages made up of vectors which lead through locations called control points. • Each of these control points has define on the X and Y axes of the work plain.
- 4. DIGITAL IMAGES • Adigital image is an 2 dim-array of real numbers. • 2-D image is divided into N-rows and M-columns. • The intersection of these rows & columns is known as pixels Origin x f(x,y) y
- 5. TYPES OF DIGITALIMAGES • Binary Images (Black and White Images) • Gray scale Images • Color Images
- 6. BINARY IMAGES • Eachpixel is just Black or White. • There is only two possible values for each pixel i.e. 0 or 1.
- 7. GRAY SCALE IMAGE •Each pixel value of gray scale images normally from 0 (Black) to 255 (White)
- 8. COLOR IMAGES • Incolor images each pixel has particular color; that color being described by the amount of red, blue and green in it. • Each of these components has a range 0-255.
- 9. HISTOGRAM • Histogram ofimage describe the intensity value of pixels that occur in an image.
- 10. IMAGE NOISE • Noise,in image, is any degradation in an image signal, caused by external disturbance while an image is being sent from one place to another place via Satellite, Wireless, and Network cable.
- 11. SOURCE OF IMAGENOISE • Error occurs in image signal while an image is being sent electronically from one place to another. • Sensor heat while clicking an image. • ISO factor: ISO number indicates how quickly a camera’s sensor absorbs light, higher ISO used, mare chance of noticeable noise. • Size of the sensor.
- 12. TYPES OF IMAGENOISE • Salt and Pepper Noise • Gaussian Noise • Speckle Noise • Periodic Noise
- 13. SALT AND PEPPERNOISE • Its also known as Impulse Noise. This noise can be caused by sharp & sudden disturbances in the image signal. • Its appearance is randomly scattered white or black (or both) pixel over the image.
- 14. GAUSSIAN NOISE • GaussianNoise is caused by random fluctuations in the signal. its modeled by random values added to an image.
- 15. SPECKLE NOISE • Specklenoise can be modeled by random values multiplied by pixel values of an image.
- 16. PERIODIC NOISE • Periodicnoise is appearance when signal is subject to a periodic, rather than a random disturbance.
- 17. NEIGHBORS OF PIXEL •In a 3X3 region a pixel p at coordinate (i,j) has 2 horizontal and vertical neighbor whose coordinates are given in the figure:
- 18. SALT AND PEPPERNOISE REMOVAL • Minimum Filtering • Maximum Filtering • Mean Filtering • Rank Order Filtering • Median Filtering • New Generated Filtering
- 19. MINIMUM FILTERING • Currentpixel replace by minimum pixel value of its neighboring pixels.
- 20. MAXIMUM FILTERING • Currentpixel replace by maximum pixel value of its neighboring pixels.
- 21. MEAN FILTERING • Currentpixel replace by arithmetic mean of its neighboring pixels values.
- 22. RANK ORDER FILTERING •Current pixel replace by user define order of its neighboring pixels.
- 23. MEDIAN FILTERING • Currentpixel replace by mid element of its neighboring pixels.
- 24. NEW GENERATED FILTERING Currentpixel replace by arithmetic mean mid-1,mid,m+1of its neighboring pixels.
- 25.