1. Image processing Second lecture

2. Image Image Representation We have seen that the human visual system (HVS) receives an input image as a collection of spatially distributed light energy; this is form is called an optical image. Optical images are the type we deal with every day –cameras captures them, monitors display them, and we see them [we know that these optical images are represented as video information in the form of analog electrical signals and have seen how these are sampled to generate the digital image I(r, c). The digital image I (r, c) is represented as a two- dimensional array of data, where each pixel value corresponds to the brightness of the image at the point (r, c). in linear algebra terms, a two-dimensional array like our image model I( r, c ) is referred to as a matrix, and one row ( or column) is called a vector. The image types we will consider are:

3. Images type Binary Image Binary images are the simplest type of images and can take on two values, typically black and white, or ‘0’ and ‘1’. A binary image is referred to as a 1 bit/pixel image because it takes only 1 binary digit to represent each pixel.

4. Images type Gray Scale Image Gray _scale images are referred to as monochrome, or one-color image. They contain brightness information only brightness information only, no color information. The number of different brightness level available. The typical image contains 8 bit/ pixel (data, which allows us to have (0- 255) different brightness (gray) levels. The 8 bit representation is typically due to the fact that the byte, which corresponds to 8-bit of data, is the standard small unit in the world of digital computer.

5. Images type Gray Scale Image

6. Images type Color Image Color image can be modeled as three band monochrome image data, where each band of the data corresponds to a different color. The actual information stored in the digital image data is brightness information in each spectral band. When the image is displayed, the corresponding brightness information is displayed on the screen by picture elements that emit light energy corresponding to that particular color. Typical color images are represented as red, green,and blue or RGB images.using the 8-bit monochrome standard as a model, the corresponding color image would have 24 bit/pixel – 8 bit for each color bands (red, green and blue ). The following figure we see a representation of a typical RGB color image.

7. Images type Color Image

8. Digital Image File Format Why do we need so many different types of image file format? The short answer is that there are many different types of images and application with varying requirements. حسب التطبيقات والاحتياجات A more complete answer, also considers market share proprietary information, and a lack of coordination within the imaging industry. الجواب الاكثر اكتمال حسب حاجات السوق

9. Digital Image File Format TIFF(Tagged Image File Format) and GIF(Graphics Interchange Format): They are used on World Wide Web (WWW). GIF files are limited to a maximum of 8 bits/pixel and allows for a type of compression called LZW. The GIF image header is 13 byte long & contains basic JPEG (Joint photo Graphic Experts Group): It is simply becoming standard that allows images compressed algorithms to be used in many different computer platforms. JPEG images compression is being used extensively on the WWW. It’s, flexible, so it can create large files with excellent image equality.

10. Image processing Pre-processing Data reduction Feature analysis ما قبل المعالجة اختزال البيانات تحليل اخبارى

11. Pre-processing ROI (region of interest) ….zoom, crop, translate, rotate zoom like enlarge and sharing Algebra operation Enhancement Reducing data

12. Zoom process Zero-order-hold Zero-order-hold (row, row) Zero-order-hold (column, column) Zero-order-hold (row, column) 402010 705030 908010 40 20 10 70 50 30 90 80 10 Zero-order-hold ((column, column)

13. Zoom process 402010 402010 705030 705030 908010 908010 402010 705030 908010 402010 705030 908010 40 20 10 40 20 10 70 50 30 70 50 30 90 80 10 90 80 10 Zero-order-hold (column, column) Zero-order-hold (row, column)

14. Zoom process Zoom processing by finding the average Average (row, row) Average (column, column) Average (row, column) 848 484 882 86468 46864 85852 Average (row, row)

15. Zoom process Convolution First step 357 276 349 0000000 0305070 0000000 0207060 0000000 0304090 0000000

16. Zoom process Convolution second step : define new mask that we going to use it 1/41/21/4 1/21 1/41/21/4

17. Zoom process Convolution 1/41/21/4 1/21 1/41/21/4 0000000 0305070 0000000 0207060 0000000 0304090 0000000

18. Zoom process Convolution Results 34567 17/4625/413/25/2 9/2713/262 411/226/415/25/2 7/2413/293

19. Zoom process Zoom by using k factor Example [125 140 155] when k=3, Sol 140-125=15 15/3=5 125+5=130 When k=3, so we add 5 again to 130 (k-1) Result [125 130 135 140 145 150 155]

20. Zoom process Homework Example: if u have following matrix, what the result of enlarging this matrix? Assume k=4 140160180 160180200

21. Image algebra Algebra operation Arithmetic: (+, *, -, / ) Logical operation: (OR, AND, NOT… gates)