Fundamental steps in image processing

Fundamental steps in image processing

1. 20CSE525J DIGITA L IMAG E PROCESSING UNIT-1

2. UNIT-1 Topic 2 : FUNDAMENTAL STEPS IN DIGITA L IMAGE PROCESSING

3. What is Image ? A n image i s a n arr a y , o r a mat rix pixels (picture elements) arranged in columns and rows. A n im a ge i s a spatial repr e sentation o f a tw o - dimensional or three-dimensional scene.

4. Image Types RGB 3 Arrays - RED , GREEN ,BLUE Combination RGB formed other colors. Range (0- 255) 8 bits INDEXED Only one index array Similar to Text book index One index number which holds RGB levels GRAY SCALE Only one array It is seen in XRAYS,SCAN,CT etc which is used in Image Processing Range (0 -255 ) ,only Gray shades. BW Range (0,1) or (0-255) 0 – BLACK 1 - WHITE

5. WHY…..digital image processing …??? I m provement o f pic t orial i n for m ation for human interpretation Processing of image data for storage, transmission, and representation for autonomous machine perception

6. FUNDAMENTAL STEPS IN DIGITA L IMAGE PROCESSING 6

7. Steps involved in image processing (1) Image Acquisition - retrieving an image from some source, usually hardware based source for processing - Image acquisition involves pre-processing such as scaling. Image Enhancement - To improve the quality of the image for future processing Image Restoration -To restore the image which is affected by noise

8. Steps involved in image processing Color image processing it is gaining importance as there is significant increase in the use of digital image Wavelets and multi resolution processing Representation of images in various degrees of resolution Compression - Techniques required for reducing the storage required to save an image and bandwidth required to transmit. Morphological Processing -Deals with tools for extracting image components

9. Steps involved in image processing (8) Segmentation (10) - Partitionin g a n imag e int o image s which several requires individual object recognition (9) Representation and Description - Always follows the output of segmentation process ex: chart, graph Object Recognition - Process that assigns a label to an object based on the descriptors

10. Key Stages in Digital Image Processing: Image Acquisition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing

11. (1) Image Acquisition Mostly ,the captured images are analog. Convert analog image to digital image

12. Sampling & Quantization 12 Sampling Digitizing the coordinate values is called sampling Measuring the brightness information only at a discrete spatial location Quantization Digitizing the amplitude values is called quantization involves representing the sampled data by a finite number of levels based on some criteria such as minimization of quantizer distortion .

13. Key Stages in Digital Image Processing: Image Enhancement Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing

14. (2) Image Enhancement Image Enhancement : Process of manipulating an image so that the result is more suitable than the original image for a specific application. (i.e.) It is Application Specific . (i.e.) Enhancement Techniques are problem oriented . Viewe r i s th e ultimat e judg e fo r image enhancement techniques.

15. Image Enhancement It includes sharpening of images Brightness Contrast adjustment Removal of noise It is “ subjective ” in nature, for example ,some people like high saturation images and some people like natural colors

16. Enhancement Techniques :Spatial Domain Gray level Transformation Histogram Processing Spatial Filtering Smoothing Filters Sharpening Filters

17. Enhancement Techniques :Frequency Domain Fourier Transform Smoothing Frequency Domain Filters Sharpening Filters Homomorphic Filtering

18. Examples of Image Enhancements – (i) A Cell Image of a cell corrupted by electronic noise . Resul t afte r averagin g several noisy images (a common technique for noise reduction)

19. Examples of Image Enhancements – (ii) An X-Ray An original x-ray image Result possible after contrast and edge enhancement

20. Key Stages in Digital Image Processing: Image Restoration Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing

21. (3) Image Restoration It is a process that attempts to reconstruct or recover an image. similar to enhancement :Improve the quality of the image. Removal of blur by using a deblurring function is considered as a restoration technique . Fig: Restored image Fig: Degraded image

22. Image Degradation To estimate Degradation function H for the image restoration, 1.Observation 2.Exprementation 3.Mathematical Modeling

23. Image Restoration Contd….. To reconstruct the original image from a degraded image. INPUT IMAGE “f” DEGRADATIO N FUNCTION NOISE DEGRADED IMAGE “g” FILTER RESTORED IMAGE Blurre d Image Degrade d image Restore d image Origina l Image

24. What is Image Restoration? 24 The purpose of image restoration is to restore a degraded/distorted image to its original content and quality. Ultimate goal of image restoration techniques To improve an image in some predefined sense To obtain an estimate of the original image

25. Differences between Image Enhancement and Image Resoration S.No . Image Enhancement Image Restoration 1. As the name suggests, in Image Enhancement, the original image is processed so that the resultant image is more suitable than the original for specific applications. The aim of image restoration is to bring the image towards what it would have been if it had been recorded without degradation . 2. Image enhancement makes a picture look better , without regard to how it really truly should look. Image restoration tries to fix the image to get back to the real, true image. 3. Image enhancement means improving the image to show some hidden details. Image restoration means improving the image to match the original image. 4. Image enhancement is a purely subjective processing technique. Image restoration is an objective process. 5. Image enhancement is a cosmetic procedure i.e. it does not add any extra information to the original image . It merely improves the subjective quality of the images by work in with the existing data. Restoration tries to reconstruct by using a priori knowledge of the degradation phenomena . Restoration hence deals with getting an optimal estimate of the desired result

26. Key Stages in Digital Image Processing: Colour Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Image Enhancement Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

27. (4) Color Image Processing Color is used as the basis for extracting features of interest in an image Color image processing is an area that has been gaining its importance because of the significant increase in the use digital image.

28. (5) Wavelets and M ultiresolution Processing Wavelets are the foundations for representing images in various degree of resolution .

29. Key Stages in Digital Image Processing: Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Image Compression Colour Image Proces s i ng

30. Compression techniques are used to reduce the redundant information in the image data in order to facilitate the storage, transmission and distribution of images (e.g. GIF, TIFF, PNG, JPEG) Storage and transmission of digital multimedia systems is a major problem High quality image data requires large amount of storage space and transmission bandwidth One best solution is to compress the information (6) Image Compression

31. Types of Image Compression Lossless compression or Reversible compression Lossy compression or irreversible compression

32. Lossless Compression Image after compression and decompression is identical to the original image Lossless compression doesn’t reduce the quality of the file at all. Every bit of information is preserved during decompression But compression ratio is less Preferred in medical image compression

33. Lossy Compression Reconstructed image contains degradation with respect to original image Once a file has been compressed using lossy compression, the discarded data cannot be retrieved again . High compression ratio is achieved Preferred in multimedia applications

34. Key Stages in Digital Image Processing: Morphological Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Image Images taken from Gonzalez & Woods, Digital Image Processing Processing Compression

35. ( 7) Morphological Processing Extract image components that are useful in the representation and description of region shape . Morphological operations apply a structuring element to an input image, creating an output image of the same size.

36. Morphological Processing T he basic morphologica l operations are dilation and erosion. Dilation adds pixels to the boundaries of objects in an image, while erosion removes pixels on object boundaries. The number of pixels added or removed from the objects in an image depends on the size and shape of the structuring element used to process the image.

37. Morphological Operations In the morphological dilation and erosion operations, the state of any given pixel in the output image is determined by applying a rule to the corresponding pixel and its neighbors in the input image. The rule used to process the pixels defines the operation as a dilation or an erosion.

38. Rules for Dilation The value of the outpu t pixe l is the maximum value of all the pixels in the input pixel's neighborhood. In a binary image, if any of the pixels is set to the value 1, the output pixel is set to 1.

39. Rules for Erosion The value of the outpu t pixe l is the minimum value of all the pixels in the input pixel's neighbourhood. In a binary image, if any of the pixels is set to the value , the output pixel is set to .

40. Key Stages in Digital Image Processing: Segmentation Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Image Images taken from Gonzalez & Woods, Digital Image Processing Processing Compression

41. (5) Segmentation It is the process of partitioning a digital image into multiple segments. Used to locate objects and boundaries in an image Autonomous segmentation is one of the most difficult task in image processing

42. Key Stages in Digital Image Processing: Object Recognition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Image Images taken from Gonzalez & Woods, Digital Image Processing Processing Compression

43. (5) Object Recognition Object Detection is the process of finding instances of objects in images. This allows for multiple objects to be identified and located within the same image. Object recognition can be termed as identifying a specific object in a digital image or video. Object recognition have immense of applications in the field of monitoring and surveillance, medical analysis , robot localization and navigation etc.

44. Key Stages in Digital Image Processing: Representation & Description 44 Image Acquisition Image Restoration Morphological Processing Segmentation Image Enhancement Object Recognition Images taken from Gonzalez & Woods, Digital Image Processing Representation & Description Problem Domain Colour Image Processing Image Compression

45. (9) Image Representation & Description Image representation & description: After an image is segmented into regions ; the resulting aggregate of segmented pixels is represented & described for further computer processing. Representing regions in 2 ways: – Based on their external characteristics ( its boundary): eg : Corners – Shape characteristics Based on their internal characteristics (its region): – Regional properties: color, texture, and … Both

46. (9) Image Representation & Description Description deals with extracting attributes that results in some quantitative information of interest. It is used for differentiating one class of objects from others.

47. Image Processing Applications 47 Medical field: X-ray (or other biomedical) image enhancement. Aerial and satellite image enhancement: agriculture, weather and military Industrial applications: computer-based product inspection. Law enforcement: fingerprint processing, surveillance camera processing

48. Space applications Remote Earth resources observations Astronomy CAT X-rays Biological sciences Nuclear medicine Image Processing Applications

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