CS482 Selected Topics in Digital Image Processing بسم الله الرحمن الرحيم Instructor: Dr. Abdullah Basuhail,CSD, FCIT, KAU, 1432H Chapter 2: Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 2 Visual Perception  Mach bands: visual system tends to undershoot or overshoot around the boundary of regions of different intensities  Simultaneous contrast: region’s perceived brightness does not depend on its intensity  Optical illusions: eye fills in nonexisting information or wrongly perceives geometrical properties of objects

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 6 Representing Digital Images  Digital image  M  N array  L discrete intensities – power of 2  L = 2 k  Integers in the interval [0, L - 1 ]  Dynamic range: ratio of maximum / minimum intensity  Low: image has a dull, washed-out gray look  Contrast: difference between highest and lowest intensity  High: image have high contrast

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 8 Representing Digital Images  Digital image  # bits to store : b = M  N  k  When M = N: b = N 2 k  k-bit image: e.g. an image with 256 possible discrete intensity values is called an 8-bit image

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 10 Spatial and Intensity Resolution  Resolution: dots (pixels) per unit distance  dpi: dots per inch

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 12 Variation of Number of Intensity Levels  Reducing the number of bits from k=7 to k= 1 while keeping the image size constant  Insufficient number of intensity levels in smooth areas of digital image leads to false contouring

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 15 Image Interpolation  Using known data to estimate values at unknown locations  Used for zooming, shrinking, rotating, and geometric corrections  Nearest Neighbor interpolation  Use closest pixel to estimate the intensity  simple but has tendency to produce artifact  Bilinear interpolation  use 4 nearest neighbor to estimate the intensity  Much better result  Bicubic interpolation  Use 16 nearest neighbors of a point

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals 16 Interpolation works by using known data to estimate values at unknown points. For example: if you wanted to know the temperature at noon, but only measured it at 11AM and 1PM, you could estimate its value by performing a linear interpolation: If you had an additional measurement at 11:30AM, you could see that the bulk of the temperature rise occurred before noon, and could use this additional data point to perform a quadratic interpolation:

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals The more temperature measurements you have which are close to noon, the more sophisticated (and hopefully more accurate) your interpolation algorithm can be.

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals Nearest neighbor is the most basic and requires the least processing time of all the interpolation algorithms because it only considers one pixel — the closest one to the interpolated point. This has the effect of simply making each pixel bigger. NEAREST NEIGHBOR INTERPOLATION

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals BILINEAR INTERPOLATION Bilinear interpolation considers the closest 2x2 neighborhood of known pixel values surrounding the unknown pixel. It then takes a weighted average of these 4 pixels to arrive at its final interpolated value. This results in much smoother looking images than nearest neighbor. The diagram to the left is for a case when all known pixel distances are equal, so the interpolated value is simply their sum divided by four.

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals BICUBIC INTERPOLATION Bicubic goes one step beyond bilinear by considering the closest 4x4 neighborhood of known pixels — for a total of 16 pixels. Since these are at various distances from the unknown pixel, closer pixels are given a higher weighting in the calculation. Bicubic produces noticeably sharper images than the previous two methods, and is perhaps the ideal combination of processing time and output quality. For this reason it is a standard in many image editing programs (including Adobe Photoshop), printer drivers and in-camera interpolation.

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals The intensity value assigned to point (x,y) is obtained by equation where the sixteen coefficients are determined from the sixteen equations in sixteen unknowns that can be written using the sixteen nearest neighbours of point (x,y)

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 23 Arithmetic Operations  Array operations between images  Carried out between corresponding pixel pairs  Four arithmetic s(x, y) = f(x, y) + g(x, y) d(x, y) = f(x, y) – g(x, y) p(x, y) = f(x, y)  g(x, y) v(x, y) = f(x, y) ÷ g(x, y)  e.g. Averaging K different noisy images can decrease noise  Used in the field of astronomy

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals Averaging K different noisy images can decrease noise. Used in astronomy

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals Enhancement of difference between images using image subtraction

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals Mask mode radiography

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals Shading correction by image multiplication (and division)

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals Masking (ROI) using image multiplication

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 29 Arithmetic Operations  To guarantee that the full range of an arithmetic operation between images is captured into a fixed number of bits, the following approach is performed on image f f m = f – min(f) which creates an image whose minimum value is 0. Then the scaled image is f s = K [ f m / max(f m )] whose value is in the range [0, K]

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 30 Set and Logical Operations  Sets can be used to let the elements of sets be the coordinates of pixels (ordered pairs of integers) representing regions (objects) in an image  Union  Intersection  Complement  Difference  Logical operations  OR  AND  NOT  XOR

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 34 Spatial Operations  Single-pixel operations  For example, transformation to obtain the negative of an 8- bit image S = T (z)

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 36 Spatial Operations  Neighborhood operations  For example, compute the average value of the pixels in a rectangular neighborhood of size m  n centered on ( x, y )

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 38 Spatial Operations  Geometric spatial transformations  Called rubber-sheet transformations  Consists of two operations  Spatial transformation of coordinates e.g. ( x, y ) = T { ( v, w ) } = ( v /2, w /2)  Affine transform: scale, rotate, transform, or sheer a set of points  Intensity interpolation

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 42 Vector and Matrix Operations  RGB images  Multispectral images

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

CS382, CSD, FCIT, KAU, KSABased on textbook: Digital Image Processing, 3 rd Edition, by Gonzalez and Woods Compiled by: Dr. Abdullah Basuhail, 1432H 44 Image Transforms  Image processing tasks are best formulated by  Transforming the images  Carrying the specified task un a transform domain  Applying the inverse transform

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals

Digital Image Processing, 3rd ed. © 1992–2008 R. C. Gonzalez & R. E. Woods Gonzalez & Woods Chapter 2 Digital Image Fundamentals Chapter 2 Digital Image Fundamentals