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- 1. 24-09-2013©Kalyan Acharjya 1 Introduction to Digital Image Processing Digital Image Processing in MATLAB Two Live Applications of Digital Image Processing 75 Min 120 Min 30 Min ©KALYANACHARJYA
- 2. A Lecture on Introduction to DIGITAL IMAGE PROCESSING 24-09-2013©Kalyan Acharjya 2 Presented By Kalyan Acharjya Assistant Professor, Dept. of ECE Jaipur National University
- 3. 25-09-2013©Kalyan Acharjya 3 Sorry, shamelessly I opened the lock without prior permission taken from the original owner. Some images used in this presentation contents are copied from internet without permission. Only Original Owner has full rights reserved for copied images. This PPT is only for fair academic use. Kalyan Acharjya
- 4. 24-09-2013©Kalyan Acharjya 4 Objective of Two Hour Presentation “To introduce the basic concept of Digital Image Processing”
- 5. Contents 24-09-2013©Kalyan Acharjya 5 What is image and Image Processing? Image Understanding. Why Digital image Processing ? Digitization of Image. Histogram and Thresholding of Image. Noise and its Extraction from Image. Edge Detection. Image Enhancement. Image Compression. Data Hiding in Image. Color Image Processing. Applications
- 6. 24-09-2013©Kalyan Acharjya 6 What is image ? And Image Understanding.
- 7. What is image? 24-09-2013©Kalyan Acharjya 7 An image is a 2-D light intensity function f( x, y). An image is considered as Matrix. A digital image f( x, y) is described both in spatial co-ordinates and Brightness. • The points in the image and element value of matrix identifies gray level value at that point. This element is called pels or Pixels. • So f( x, y)=R( x, y) *I(x, y) Where R Reflectivity of Surface (Pixel Point) I Intensity of incident Light y x
- 8. Matrix or Digital Representation of Image 24-09-2013©Kalyan Acharjya 8 • An Image has infinite intensity value. • Also infinite picture point -How its stored. • Digitization of image. Spatial discretization by Sampling. Intensity discretization by Quantization. I=
- 9. Matrix as an Image 24-09-2013©Kalyan Acharjya 9 An Matrix is an image for DIP
- 10. Types of Digital Images 24-09-2013©Kalyan Acharjya 10 Binary Image: Each Pixel is just Black and white, i.e. 0 or 1 <462x493 logical> Gray Scale Image: Each Pixel is shade of Gray, its 0(black) to white(255),i.e. each pixel~8 bits <462x493 unit8> Color Image or RGB Image, Each pixel corresponds to 3 values. <462x493x3 unit8>
- 11. Image Data Type 24-09-2013©Kalyan Acharjya 11 int8-8 bit integer, Range -128 to 127 unit8-8 bit unsigned integer, Range 0 to 225 int16-16 bit integer, Range -32768 to 32767 uint16-16 bit unsigned integer, Range 0 to 65535 double-Double precision real number, Machine Specific
- 12. Levels of Image Understanding 24-09-2013©Kalyan Acharjya 12 Low Level-Involve primitive operations. e.g. Image Preprocessing, noise reduction, Enhancement etc. Image input - Image Out • Mid Level- Image segmentation, identify particular objects. Image input - Attributes extracted from those images e.g. edges, contour, identify etc. • High Level-Involving making sense of an ensemble of recognize objects, image analysis and far end the functions normally associated with human vision. Image • Processing Image • Analysis Image • Measurements
- 13. 24-09-2013©Kalyan Acharjya 13 Lets look back ! When the Digital Image Processing Started ?
- 14. History of Image Processing 24-09-2013©Kalyan Acharjya 14 Its not young field, In 1920 submarine cables were used to transmit digitalized newspaper pictures between London and New-Work-Use Telegraphic Printing. In 1921 –Improved in printing, use photographic printing to enhance the quality and resolution. Actually DIP/ Computer Processing Technique was used to improve the pictures of moon transmitted by RANGER 7 at JET PROPULSION LAB. it’s the real beginning…
- 15. 24-09-2013©Kalyan Acharjya 15 Why Digital Image Processing ?
- 16. Why Digital Image Processing 24-09-2013©Kalyan Acharjya 16 How we stored the image: Reduce the size for storage . How analog image world is relate to digital processing world. Compression-Remove redundancies. Transmission with minimum bandwidth. Lossy Compression=redundancy +some information, but still acceptable. Original Image Size-116 KB Compressed Image Size-12.9 KB, 11 % Compressed Image Size-1.95 KB, 1.6 %
- 17. Why Digital Image Processing 24-09-2013©Kalyan Acharjya 17 Image Enhancement :To improve the interpretability or perception of information in image. Spatial Domain Method. Frequency Domain Method. • Moving Object Tracking • Human-Computer Interaction • Computer Vision etc. Lena Central Compressed Spatial Domain Frequency Domain
- 18. 24-09-2013©Kalyan Acharjya 18 How Digitization of Image ?
- 19. Lets, little detail : Digitization of Image 24-09-2013©Kalyan Acharjya 19 a b a b a b a b Fig-A-Continuous Image Fig-B-Gray level Variation from a to b Fig-C-Sampling and Quantization Fig-D-Digital line from a to b
- 20. Bit Planes of Grey Scale image 24-09-2013©Kalyan Acharjya 20 Grey scale images can be transformed into a sequence of binary images by breaking them into bit planes.
- 21. Spatial and Gray Level Resolution 24-09-2013©Kalyan Acharjya 21 • Gray level L=2k • L is discrete level allowed to each pixel. • M and N are spatial • Halve and Double • The number of bits required to store digital image b=MxNxk • When M=N, b= kN2
- 22. 24-09-2013©Kalyan Acharjya 22 Basics operations with image.
- 23. Arithmetic and Logical Operations in images 24-09-2013©Kalyan Acharjya 23 Say Image as y=f(x) This include add or subtract or multiply or divide each pixel value by constant factor, which may be pixel value of another image. Y=f(x)+/-/*c Complement: For gray scale image is its photographic negative. Logical Operations: AND,OR,NOT in binary image.
- 24. Resize an Image 24-09-2013©Kalyan Acharjya 24 Interpolation Extrapolation
- 25. 24-09-2013©Kalyan Acharjya 25 Histogram of Image.
- 26. Histograms of Images 24-09-2013©Kalyan Acharjya 26 Histograms of Gray level image represents the numbers of times each gray level occurs in the image. Dark image-the gray levels would be clustered at the lower end In a Uniformly bright image, the gray levels would be clustered at the upper end. In a well contrasted image, the gray levels would be well spread out over much of the range.
- 27. Importance of Histograms Graph 24-09-2013©Kalyan Acharjya 27 In Poorly Contrast image, enhance by spreading out of its histograms. There are two ways- Histograms stretching (contrast Stretching). Histogram Equalization. Histograms stretching: • Poorly contrasted image in the range [a, b] • Stretch the gray levels in the center of the range out by applying a piecewise linear function. • This function has the effect of stretching the gray levels [a, b] to [c, d], where a<c and d>b
- 28. Histograms stretching (Cont.) 24-09-2013©Kalyan Acharjya 28 The linear function imadjust(I, [a, b],[c, d]) if Pixel value is less than c are all converted to c and pixel values greater than d are all converted to d. a b 1 c d 1 Gamma<1 Gamma>1 Y= ( 𝑥−𝑎 𝑏−𝑎 )^Gamma (d-c)+c
- 29. Gamma Scaling 24-09-2013©Kalyan Acharjya 29 Its important for graphics and games, its relates to pixel intensities of the image. One horn RHINO at Kaziranga National Park, Assam
- 30. Histograms Equalization 24-09-2013©Kalyan Acharjya 30 Histogram equalization is a technique for adjusting image intensities to enhance contrast • Histogram equalisation algorithm: Let be the intensities of the image, and let be its normalised histogram function. The intensity transformation function for histogram equalisation is That is, we add the values of the normalised histogram function from 1 to k to find where the intensity will be mapped. Notice that the range of the equalised image is the interval [0,1]. mkrk ,...,2,1, )( krp k j kk rprT 1 )()( kr
- 31. Histogram Equalization 24-09-2013©Kalyan Acharjya 31 Original Image and It’s Histogram Histogram Equalized Image and It’s Histogram
- 32. Thresholding of an image 24-09-2013©Kalyan Acharjya 32 Single Threshold: A gray scale image is turned into a binary image by first choosing a gray level T in the original image. Pixel Value>T tends to white (1) Pixel Value<=T tends to black (0) • Double Threshold: A pixel becomes white if T1<pixel value<T2. A pixel becomes black if gray level is others.
- 33. 24-09-2013©Kalyan Acharjya 33 Noise Extraction from Image.
- 34. Noise and Its Extraction 24-09-2013©Kalyan Acharjya 34 Noise is any degradation in the image signal caused by external disturbance. Salt and pepper noise: It is caused by sharp and sudden disturbances in the image. Gaussian noise: It is caused by random fluctuations in the signal. It can be idealized form of white noise. Speckle noise: it is modeled by random values multiplied by pixel values. In radar applications. Shot noise: The dominant noise in the lighter parts of an image from an image sensor.
- 35. Removal of noise by Filtering 24-09-2013©Kalyan Acharjya 35 Linear filter Median Filter Specific case of order statistic filtering. Remove salt & pepper noise. Adaptive Filter Weiner filter use to remove Gaussian noise.
- 36. Extract Noise from an image 24-09-2013©Kalyan Acharjya 36 Image with Gaussian Noise Image after Noise removal ‘wiener2’ Filter
- 37. Spectral Filtering 24-09-2013©Kalyan Acharjya 37 Spectral filtering is most commonly used to either select or eliminate information from an image based on the wavelength of the information. Spectral selectivity is a technique for creating images which uses intentionally limited ranges of radiation in the ultraviolet, visible or infrared portions of the spectrum
- 38. Example High Pass Filtering 24-09-2013©Kalyan Acharjya 38 OriginalImage HighPassfilteringresult Highfrequency emphasisresult Afterhistogram equalisation
- 39. 24-09-2013©Kalyan Acharjya 39 Edge Detection of Image ?
- 40. What is Image Edge 24-09-2013©Kalyan Acharjya 40 Edges are those places in an image that correspond to object boundaries. Edges are pixels where image brightness changes abruptly. It is a vector variable (magnitude of the gradient, direction of an edge) .
- 41. Steps of Edge Detection 24-09-2013©Kalyan Acharjya 41 Filtering – Filter image to improve performance of the Edge Detector with respect to noise Enhancement – Emphasize pixels having significant change in local intensity Detection – Identify edges - Thresholding Localization – Locate the edge accurately, estimate edge orientation Types of Edges Step Edge Ramp Edge Line Edge Roof Edge
- 42. Edge Detection 24-09-2013©Kalyan Acharjya 42 Hey its our TAJMAHAL…! What you have done…?
- 43. Edge Detection 24-09-2013©Kalyan Acharjya 43 Motivation: Detect changes in the pixel value as large gradient. I(m , n)={ 1 𝑔 𝑚, 𝑛 > 𝑇ℎ 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 Image x(m , n) Edge Map I(m, n) Prewitt Operator. Sobel Operator. Canny Edge Detector. Kirsch Compass Masks. Roberts Operator Gradient Operator Thresholding
- 44. Basics Relationship Between Pixels 24-09-2013©Kalyan Acharjya 44 Neighbors of pixel Adjacency, Connectivity 4 Adjacency. 8 Adjacency. m Adjacency. Image Operations Point Local Global Region and Boundaries. Distance between Pixel Image operation on pixel basis.
- 45. 24-09-2013©Kalyan Acharjya 45 Image Enhancement.
- 46. Image Enhancement Technique 24-09-2013©Kalyan Acharjya 46 Basic Gray level transformation Histogram Modification Average and Median Filtering Frequency domain operations. Homomorphic Filtering. Edge Enhancement. Image Enhancement Technique Better Image Spatial or Frequency Domain
- 47. Spatial Domain 24-09-2013©Kalyan Acharjya 47 Point of interest is f( x, y) Contrast stretching All these point operation, hence its point processing. f(x, y) y x r s r > Input gray level s > Output Gray level s=T(r) s=T(r) s r Threshold
- 48. Image Enhancement in Frequency Domain 24-09-2013©Kalyan Acharjya 48 • To filter an image in the frequency domain: Compute F( u, v) the DFT of the image Multiply F( u , v) by a filter function H( u, v) Compute the inverse DFT of the result
- 49. DFT of Image 24-09-2013©Kalyan Acharjya 49 • The DFT of a two dimensional image can be visualised by showing the spectrum of the images component frequencies. DFT y x v u
- 50. Basic Frequency Domain Filters 24-09-2013©Kalyan Acharjya 50 Low Pass Filter High Pass Filter
- 51. Ex. of Image Enhancement in FD 24-09-2013©Kalyan Acharjya 51 • Different low pass Gaussian filters used to remove blemishes in a photograph.
- 52. Frequency Domain Laplacian Example 24-09-2013©Kalyan Acharjya 52 Original Image Laplacian Filtered Image Laplacian Image Scaled Enhanced image
- 53. Conclusion of Filtering 24-09-2013©Kalyan Acharjya 53 Fourier transform in Image Processing in the frequency domain Image smoothing Image sharpening Fast Fourier Transform Image restoration using the spatial and frequency based techniques.
- 54. 24-09-2013©Kalyan Acharjya 54 Information Hiding By Image Processing. ©KALYANACHARJYA
- 55. Information Hiding by Image Processing 24-09-2013©Kalyan Acharjya 55 Steganography It is the process of hiding of a secret message within an ordinary image. • Watermarking It is the process of hiding of a secret message within an ordinary image, but carrier image must be unchanged. Encoder Decoder
- 56. JPEG 24-09-2013©Kalyan Acharjya 56 Image Compression. Size-270 KB Size-22 KB “Without Compression a CD store only 200 Pictures or 8 Seconds Movie”
- 57. Image Compression-Lossy or Lossless 24-09-2013©Kalyan Acharjya 57 Image compression is the process of reducing the amount of data required to represent an image. But its resolution or features should be unchanged for human perception. Relative Data Redundancy Rd of the first data set is Rd=1-1/CR where CR-Compression Ratio=n1/n2 ,n1 and n2 denote the nos. of information carrying units in two data sets that represent the same information. • In Digital Image Compression , the basics data redundancies are Coding Redundancy Inter pixel Redundancy Psycho-visual Redundancy
- 58. Image Compression General Models 24-09-2013©Kalyan Acharjya 58 Some image Compression Standard JPEG-Based on DCT JPEG 2000-Based on DWT GIF-Graphics Interchange Format etc. Source Encoder Channel Encoder Channel Decoder Source Decoder Channel/ Store F(x, y) F’(x, y)
- 59. Data ≠ Information 24-09-2013©Kalyan Acharjya 59 Data and information are not synonymous terms! Data is the means by which information is conveyed. Data compression aims to reduce the amount of data required to represent a given quantity of information while preserving as much information as possible. Image compression is an irreversible process. Some Transform used for Image Compression DCT-Discrete Cosine Transform DWT-Discrete wavelet Transform etc
- 60. 24-09-2013©Kalyan Acharjya 60 Color Image Processing
- 61. Color Image Processing 24-09-2013©Kalyan Acharjya 61 • RGB : Color Monitor, Color Camera, Color Scanner • CMY : Color Printer, Color Copier • YIQ : Color TV-Y(Luminance), I(In phase), Q(Quadrature) HSI, HSV
- 62. Color Issue of an Image 24-09-2013©Kalyan Acharjya 62 Red, Green and Blue Color cube Consider Each element=8 bit R,G,B ~0 to 255 Grey scale f(x , y , L) 256 Grey shades Color Scale f(x , y, r , g , b)-24 bit 255x255x255=16777216 colors
- 63. What a color image contains 24-09-2013©Kalyan Acharjya 63
- 64. RGB Components of an Image 24-09-2013©Kalyan Acharjya 64
- 65. CMY and CMYK Color Model 24-09-2013©Kalyan Acharjya 65 Cyan(C), Magenta(M) and Yellow(Y) are the secondary colors of light. • Or CMY are Primary colors of pigments. RGB to CMY Black=Cyan + Magenta + Yellow Printing Industry used to four color Printing. Cyan, Magenta, Yellow plus Black. B G R Y M C 1 1 1
- 66. 24-09-2013©Kalyan Acharjya 66 Where you start ? Digital Image Processing !
- 67. Popular Image Processing Software Tools 24-09-2013©Kalyan Acharjya 67 CVIP tools (Computer Vision and Image Processing tools) Intel Open Computer Vision Library Microsoft Vision SDL Library MATLAB KHOROS
- 68. 24-09-2013©Kalyan Acharjya 68 Applications of Digital Image Processing? ©KALYANACHARJYA
- 69. Applications of Digital Image Processing 24-09-2013©Kalyan Acharjya 69 Identification. Robot vision. Steganography. Image Enhancement. Image Analysis in Medical. Morphological Image Analysis. Space Image Analysis. IC Industry……….etc.
- 70. 24-09-2013©Kalyan Acharjya 70
- 71. 24-09-2013©Kalyan Acharjya 71
- 72. 24-09-2013©Kalyan Acharjya 72 https://twitter.com/Kalyan_online Email-kalyan.acharjya@gmail.com

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