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Ch2: Digital Image Fundamentals

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Ch2: Digital Image Fundamentals Image Processing Ch2: Digital image Fundamentals Prepared by: Hanan Hardan Hanan Hardan 1 Image Acquisition: The image is captured by a sensor (eg. Camera), and digitized if the output of the camera or sensor is not already in digital form, using analogue-to-digital convertor Hanan Hardan 2 Image sampling and quantization In order to process the image, it must be saved on computer. The image output of most sensors (eg: Camera) is continuous voltage waveform. But computer deals with digital images not with continuous images, thus: continuous images should be converted into digital form. Hanan Hardan 3 Image sampling and quantization To convert continuous image (in real life) to digital image (in computer) we use Two processes: 1.sampling 2.quantization. Remember that: the image is a function f(x,y), x and y: are coordinates F: intensity value (Amplitude) Sampling: digitizing the coordinate values Quantization: digitizing the amplitude values Thus, when x, y and f are all finite, discrete quantities, we call the image a digital image. Hanan Hardan 4 How does the computer digitize the continuous image? Ex: scan a line such as AB from the continuous image, and represent the gray intensities. Hanan Hardan 5 How does the computer digitize the continuous image? Sampling: digitizing coordinates Quantization: digitizing intensities Gray-level scale that divides gray-level into 8 discrete levels Quantization: converting each sample gray- level value into discrete digital quantity. sample is a small white square, located by a vertical tick mark as a point x,y Hanan Hardan 6 How does the computer digitize the continuous image? Now: the digital scanned line AB representation on computer: The continuous image VS the result of digital image after sampling and quantization Hanan Hardan 7 Digital Image Representation The result of sampling and quantization is a matrix of real numbers Assume that an image f(x,y) is sampled so that the resulting image has M rows and N columns. We say that the image is of size M x N. The values of the coordinates (x,y) are discrete quantities. For clarity, we use integer values for these discrete coordinates. Hanan Hardan 8 Digital Image Representation Images as Matrices Each element of this array is called an image element, picture element, pixel or pel. A digital image can be represented naturally as a MATLAB matrix: Hanan Hardan 9 Pixels! Every pixel has # of bits (k) so, the gray intensities ( L ) that the pixel can hold, is calculated according to a number of pixels it has (k). L= 2k Q: Suppose a pixel has 1 bit, how many gray levels can it represent? Answer: 2 intensity levels only, black and white. Bit (0,1) 0:black , 1: white Q:Suppose a pixel has 2 bit, how many gray levels can it represent? Answer: 4 gray intensity levels 2Bit (00, 01, 10 ,11). Now .. if we want to represent 256 intensities of grayscale, how many bits do we need? Answer: 8 bits which represents: 28=256 Hanan Hardan 10 Number of storage of bits: N * M: the no. of pixels in all the image. K: no. of bits in each pixel L: grayscale levels the pixel can represent L= 2K all bits in image= N*N*k Hanan Hardan 11 Number of storage of bits: EX: Here: N=32, K=3, L = 23 =8 # of pixels=N*N = 1024 . (because in this example: M=N) # of bits = N*N*K = 1024*3= 3072 N=M in this table, which means no. of horizontal pixels= no. of vertical pixels. And thus: Hanan Hardan 12 # of pixels in the image= N*N Spatial and gray-level resolution Hanan Hardan 13 Spatial and gray-level resolution Resolution: How Much Is Enough? This all depends on what is in the image and what you would like to do with it Key questions include Can you see what you need to see within the image? Hanan Hardan 14 Resolution: How Much Is Enough? (cont…) The picture on the right is fine for counting the number of cars, but not for reading the number plate Hanan Hardan 15 Spatial resolution: Sampling is the principal factor determining the spatial resolution of an image Basically, spatial resolution is the smallest discernible detail in an image. Spatial Resolution (هي وحدة قياس ﻻصغر جزء في الصورة يمكن تمييزة بالعين.) عدد البكسﻻت في الصورة ﻻ يحدد وضوحها, فهو فقط يحد ابعاد الصورة , اما Spatial resolution هو المسؤول عن تحديد الوضوح , فكلما كانت البكسﻻت متقاربة وتحمل قيم لونيه صحيحة اكثر كان لها قدرة اعلى على توضيح معالم الصورة بشكل اوضح. Hanan Hardan 16 How to choose the spatial resolution Spatial resolution = Sampling locations Original image Original Sampled image Sampled Under sampling, weHanan lost Hardan some image details! 17 How to choose the spatial resolution : Sampled image Original image Original mm 1 No detail is lost! Spatial resolution (sampling rate) Hanan Hardan 18 Effect of Spatial Resolution Example: 256x256 pixels 128x128 pixels insufficient spatial resolution appearance of checkerboard pattern in the image Hanan Hardan 19 64x64 pixels 32x32 pixels Example: Spatial resolution الصورة في اليسار تحمل عدد بكسﻻت اكبر من الصورة في الجهه اليمين , ومع ذالك الصورة في اليسار تبدوا غير واضحة لماذا؟ Hanan Hardan 20 gray-level resolution Quantization is the principal factor determining the gray level resolution of an image Gray-level resolution refers to the smallest discernible change in gray level. وهي تعني اصغر تغيير في الكثافة )كثافة اللون الرمادي( يمكن تمييزها ورؤيتها Color depth/ levels is given by L 2k Hanan Hardan 21 Effect of Quantization Levels Example: 256 levels 128 levels 64 levels Hanan Hardan 32 levels 22 Effect of Quantization Levels (cont.) 16 levels 8 levels In this image, it is easy to see false contour. Hanan 4 levels Hardan 2 levels 23 Digital Image Types: . Binary image (B&W) . Grayscale image . Color image (RGB) Hanan Hardan 24 Common image formats include: 1 sample per point (B&W or Grayscale) 3 samples per point (Red, Green, and Blue) For most of this course we will focus on grey- scale images Hanan Hardan 25 Binary Images Binary images are images that have been quantized to two values, usually denoted 0 and 1, but often with pixel values 0 and 255, representing black and white. Hanan Hardan 26 Image Types : Binary Image Binary image or black and white image Each pixel contains one bit : 1 represent white 0 represents black Binary data 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 Hanan Hardan 27 Grayscale Images A grayscale (or graylevel) image is simply one in which the only colors are shades of gray (0 – 255) Hanan Hardan 28 Digital Image Types : Intensity Image Intensity (monochrome or gray scale) image each pixel corresponds to light intensity normally represented in gray scale (gray level). Gray scale values 10 10 16 28 9 6 26 37 15 25 13 22 32 15 87 39 Hanan Hardan 29 Color Images Color image: A color image contains pixels each of which holds three intensity values corresponding to the red, green, and blue or( RGB) Hanan Hardan 30 Digital Image Types : RGB Image Color image or RGB image: each pixel contains a vector representing red, green and blue components. RGB components 10 10 16 28 9 6 26 37 65 70 56 43 153225995413709622566778 60 90 96 67 322115 5487 473942 85 85 43 92 54 65 65 39 31 Hanan Hardan 32 65 87 99.
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