Final presentation(image enhancement system)
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The document outlines a project on developing an image enhancement system focused on improving digital image quality through various image processing techniques, including noise removal and filtering. It covers types of image processing, applications in different fields, and the advantages of digital methods. The project concludes with an emphasis on future work in video processing and a commitment to enhancing image quality by addressing noise types like Gaussian, speckle, and salt-and-pepper noise.
Final presentation(image enhancement system)
- 2. Project Members Muhammad Hammaad Zubair Roll Number 30(A) Nauman Ali Roll Number 41(A)
- 4. Introduction Image enhancement is the improvement of digital image quality, This improvement includes filtering of the image and noise removal as well, In which degraded image can be restored
- 5. Project Description And Aims The main theme of this project is to open an image and then apply the image processing techniques, these techniques may include, Noise removal filtering Image enhancement
- 6. Image Processing Definition: Image processing, deals with the type of signals for which the input is an image and the output is also an image or parameters related to the image. As its name suggest, it deals with the processing on images Image Input Image Processing Output
- 7. Types of image processing Analog images: Analog image processing refers to the alteration of image through electrical mean For example: Television image Digital image: Digital image processing refers processing of the image in digital form (0,1) For example: Digital camera
- 8. Applications of Image Processing Medical: X-ray, MRI, CAT etc Google map: satellite/aerial views of land etc Industry: inspection of items, products etc Law enforcement: finger prints etc
- 9. Advantages of digital image processing It can avoid problems such as the building up of noise and signal distortion during processing Digital image processing may be modeled in the form of multidimensional system Digital image processing provide real instant images. Processing is easy, efficient as more It has good accuracy, small physical cells and very flexible
- 10. Noise Noise is unwanted undesired variations in pixel intensity values captured or transmitted image, Gaussian noise Speckle noise Salt and pepper noise
- 11. Gaussian noise Definition: variations in intensity drawn from a Gaussian normal distribution
- 12. Speckle noise Definition: Speckle noise is a granular noise that inherently exists in and degrades the quality of the active radar and synthetic aperture radar images
- 13. SALT AND PEPPER NOISE Salt and pepper noise is also known as Impulse Noise.This noise can be caused by sharp & sudden disturbances in the image signal, Its appearance is randomly scattered white or black (or both) pixel over the image.
- 14. Filter Filter is the process of removing some unwanted signals or noise from picture We will apply some filter to remove background noise from the image Gaussian filter Mean filter Median filter Set filter manually
- 15. Gaussian Filter Gaussian filters weigh pixels based on their distance to the Location of convolution Blurring noise while preserving features of the image Smoothing the same in all directions More significance to neighboring pixels Gaussian filter removes the Gaussian noise
- 18. Gaussian cont.. 3x3 mask, Sigma 0.5
- 19. Gaussian cont.. 3x3 mask, Sigma 5
- 20. Gaussian cont.. 3x3 mask, Sigma 10
- 21. Average Filter Mean filter How it works
- 22. Average filter Cont.. Mean filter is simple, and easy to implement method for smoothing image For example: reducing the amount of intensity variation between one pixel and the next. It is open used to reduce noise in images It is linear filter
- 24. Median Filter Median filter. How it works. Remove salt and pepper noise.
- 25. Median filter The median of the pixel value of the window is computed, and then the center pixel of the window is replaced with the computed median value The calculation of median is done is first sorting all the pixel value neighborhood (either ascending or descending order) and then replacing the pixel being considered with the middle pixel value Median filter is non linear
- 26. Median Filter cont.. Neighbors of mid value by mask Arrange all in ascending order Pick the mid value and replace it with mid of mask
- 30. Project slides cont.. Speckle Noise
- 31. Project slides cont.. Salt and Pepper noise
- 32. Project slides cont.. Gaussian filter
- 33. Project slides cont.. Median filter
- 34. Project slides cont.. Average filter
- 35. Project slides cont.. Set Filter Manually
- 36. Conclusion We were working on image processing for noise reduction system which can be used for image enhancement and removing different types of noise occurred in image while transferring or scanning data. Future work will be on videos In Sha Allah
- 37.
Editor's Notes
- #12: random fluctuations in a continuous physical process.. the random voltage variations.. electrical variation
- #14: the noise is caused by errors in the data transmission. The corrupted pixels are either set to the maximum value (which looks like snow in the image) or have single bits flipped over. In some cases, single pixels are set alternatively to zero or to the maximum value