IMAGING. Images are stored by capturing the binary data using some electronic devices (SENSORS)

Transcription

1 IMAGING Film photography Digital photography Images are stored by capturing the binary data using some electronic devices (SENSORS) Sensors: Charge Coupled Device (CCD) Photo multiplier tube (PMT)

2 The CCD was invented in 1969 by Willard Boyle and George Smith at AT&T Bell Labs.

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4 A simple image formation method

5 IMAGE is defined by a two dimensional function f(x,y) where f(x,y) is the intensity or the gray level values of the image at the spatial coordinates x,y. When x, y and f are discrete, the image is called DIGITAL image A DIGITAL image is composed of finite number of elements (say 256x256) each of which has a particular location and values. These elements are called picture element, or image element or pels or pixels

6 The value or the amplitude of f at the spatial coordinate (x,y) is a positive scalar quantity whose physical meaning is determined by the source of the image. Pixel values (f) is proportional to energy radiated by the physical source. So, 0 < f ( x, y) <

7 f(x,y) depends on 1. amount of illumination of the source on the object (i(x,y)) 2. amount of illumination reflected from the object (r(x,y)) f ( x, y) = i( x, y) r( x, y) = l where 0 0 < < i( x, y) < r( x, y) < 1 and l = gray level of the monochrome image L l min L max where L L min max = = i i min max r min r max So, L L min max = = 0 L 1 ( say)

8 The interval [ 0, L 1] is defined as GRAYSCALE l l = = 0 black L 1 white 8-bit grayscale [0,2 8 1] = [0,255]

9 An image may be continuous with respect to the x- and y- and also in the amplitude. Digitization of coordinate value is called SAMPLING. Digitization of amplitude value is called QUANTIZATION.

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11 Sampling depends on arrangement of sensors to generate the image

12 Representing Digital Images : The result of sampling and quantization yields the image in form of a matrix of real numbers. x, y vary from 0,1.. and are not the actual value of the physical coordinate

13 The number of bits (b) required to store a digitized image is b = M N k For 8-bit image, k = 8 Gray level = [0,255] L = 2 8 b = M x N x 8

14 Spatial and gray level resolution Spatial resolution : Spatial resolution is the smallest distinguishable detail in an image. It depends on sampling.

15 w w A B A B A B A B Line pairs : AA and BB Distance between the line pairs = 2w No. of lines per unit length = Hence, spatial resolution = 1 2w Spatial resolution = no. of distinguishable lines/length 1 2w

16 Typical effects of varying the number of samples in a digital image (Pixel size = constant, and gray level = 256) Sub-sampling

17 Sub-sampled image is scaled to the original one

18 Gray level resolution : Refers to the smallest distinguishable change in the gray level. Gray level resolution is highly subjective and it depends on the hardware utilized to capture the image.

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22 Enhancement is needed for better representation and extraction of important information. Methods of enhancement is highly subjective.

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30 Image enhancement approach Two categories Spatial domain method Frequency domain method

31 Spatial domain method:: Spatial domain refers to the image plane and the method implies the direct manipulation of the pixels in the image. Frequency domain method Modifying the pixels in the Fourier transformed image of the original image.

32 Spatial domain process : g ( x, y) = T[ f ( x, y)] where f ( x, y) = Original image g( x, y) = Processed image T = Transformation function or Operator

33 Point Processing The simplest form of T is when neighborhood size is 1 x 1 (i.e., point processing) g ( x, y) = T[ f ( x, y)] s = T(r) where s = g( x, y) r = f ( x, y) T is the gray level transformation function

34 Gray level transformation for contrast enhancement

35 Basic transformation functions for image enhancement

36 1.Linear (negative and identity transformation) 2.Logarithmic (log and inverse-log transformation) 3.Power law (n th and n th root power transformation)

37 Image identity s=r

38 Image negative s = L 1 r For 8-bit image ; s = 255 r

39 Log transformation s = c log( 1+ r Where c = constant; r 0 )

40 Power law transformation s = γ cr where c & γ are positive constants

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42 γ correction Dark levels have to be stretched γ < 1

43 γ correction Dark levels have to be compressed γ > 1

44 Piecewise linear transformation function

45 Gray level slicing Use to HIGHLIGHT a specific range of gray levels which are often desired in an image

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47 Bit plane slicing Inner pixel gray levels can be explored by doing bit plane slicing

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