Image enhancement techniques
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The document provides an overview of image enhancement techniques, focusing on methods from the spatial and frequency domains. It explains various procedures such as point operations, thresholding, and histogram equalization, which aim to improve image quality and highlight specific features. Applications of these techniques include sharpening image features and reducing noise to enhance visual appeal.
![Spatial domain methods:
• The term spatial domain refers to the aggregate of pixels composing
an image.
• Spatial domain methods are procedures that operate directly on these
pixels.
• Spatial Domain processes will be denoted by the expression ,
g(x,y)= T[f(x,y)]
Where, g is the output, f is the input image and T is an operation on f
defined over some neighborhood of (x,y)](https://image.slidesharecdn.com/imageenhancementtechniques-181219105938/85/Image-enhancement-techniques-5-320.jpg)
![Point operation: Brightness modification
Increasing the brightness of an image:
g[m,n]=f[m,n]+k
Decreasing the brightness of an image:
g[m,n]=f[m,n]-k](https://image.slidesharecdn.com/imageenhancementtechniques-181219105938/85/Image-enhancement-techniques-8-320.jpg)
![Spatial operations:
• Operations performed on local neighborhoods of input pixels
• Image is convolved with [FIR] finite impulse response filter called
spatial mask .
• Techniques such as :
- Noise smoothing
- Median filtering
- LP and HP filtering
- Zooming](https://image.slidesharecdn.com/imageenhancementtechniques-181219105938/85/Image-enhancement-techniques-19-320.jpg)
Image enhancement techniques
- 1. BRIEF INTRODUCTION OF IMAGE ENHANCEMENT TECHNIQUES Presented By: Bulbul Agrawal M.Tech IInd year (IT Branch)
- 2. Outline: Introduction Image enhancement techniques: 1. Spatial domain 2. Frequency domain Applications Conclusion
- 3. Introduction: • Image Enhancement is the process of manipulating an image so that the result is more suitable than the original for a specific application. • The idea behind the enhancement technique is to bring out details that are hidden or simple to highlight the certain features of interest in an image.
- 5. Spatial domain methods: • The term spatial domain refers to the aggregate of pixels composing an image. • Spatial domain methods are procedures that operate directly on these pixels. • Spatial Domain processes will be denoted by the expression , g(x,y)= T[f(x,y)] Where, g is the output, f is the input image and T is an operation on f defined over some neighborhood of (x,y)
- 6. Cont… According to the operations on the image pixels, it can be further divided into 2 categories: 1. Point operations 2. Spatial operations
- 7. Point operation: • It is the process of contrast enhancement. • It is the process to produced an image of higher contrast than the original by darkening a particular level. • Enhancement at any point in an image depends only on the gray level at that point, techniques in this category are often referred to as point processing.
- 8. Point operation: Brightness modification Increasing the brightness of an image: g[m,n]=f[m,n]+k Decreasing the brightness of an image: g[m,n]=f[m,n]-k
- 9. Cont… Fig: Example of brightness modification
- 10. Point operation: Inverse transformation • Example is image negative. • Negative transform exchanges dark values for light values and vice versa. • The negative transformation is defined by, s=(L-1-r) Where, L-1=maximum pixels value and r= pixel value of an image
- 11. Cont… Fig: Example of image inversion
- 12. Point operation: Thresholding • Thresholding is required to extract a part of an image which contains all the information. • Thresholding is a part of more general segmentation problem. • Pixels having intensity lower than the threshold T are set to zero and the pixels having intensity greater than the threshold are set to 255. • This type of hard thresholding allows us to obtain a binary image from a grayscale image.
- 13. Cont… Fig: Example of thresholding
- 14. Point operation: Gray-level slicing • The purpose of gray-level slicing is to highlight a specific range of gray values. • Two different approaches can be adopted for gray-level slicing, 1. Gray-level slicing without preserving the background 2. Gray-level slicing with the background
- 15. Cont… Without preserving the background: • This displays high values for a range of interest and low values in other areas. • The main drawback of this approach is that the background information is discarded. With preserving the background: • In gray-level slicing with background, the objective is to display high values for the range of interest and original gray-level values in other areas. • This approach preserves the background of the image.
- 16. Cont… Fig: Example of gray-level slicing
- 17. Point operation: Bit plane slicing • The gray level of each pixel in a digital image is stored as one or more bytes in computer. • The three main goals of bit plane slicing are: 1. Converting a gray level image to binary image. 2. Representing an image with fewer bits and compressing the image to a smaller size. 3. Enhancing the image by focusing.
- 18. Cont… Fig: Example of bit-plane slicing
- 19. Spatial operations: • Operations performed on local neighborhoods of input pixels • Image is convolved with [FIR] finite impulse response filter called spatial mask . • Techniques such as : - Noise smoothing - Median filtering - LP and HP filtering - Zooming
- 20. Mask Operation: • Mask is a small matrix useful for blurring, sharpening, edge-detection and more. • New image is generated by multiplying the input image with the mask matrix. • The output pixel values thus depend on the neighbouring input pixel values. • The mask may be of any dimension 3X3 4X4 ….
- 21. Histogram manipulation: Histogram: • It is the another spatial domain technique. • It is the plot of frequency of occurrence of an event. • The histogram provides a convenient summary of the intensities in an image. Histogram equalization: • Histogram equalization is a method in image processing of contrast adjustment using the image’s histogram.
- 22. Cont… Fig: Example of histogram and histogram equalization
- 23. Frequency Domain Methods: • We simply compute the Fourier transform of the image to be enhanced, multiply the result by a filter, and take the inverse transform to produce the enhanced image. • Filtering are done in FDM, like low-pass, high-pass, butterworth high- pass filter, gaussian filter etc.
- 24. Applications: • Image enhancement techniques are used to sharpen image features to obtain a visually more pleasant, more detailed or less noisy output image. • Contrast enhancement can be achieved by histogram equalization. • Blur reduction
- 25. Conclusion: • The aim of image enhancement is to improve the information in images for human viewers, or to provide ‘better’ input for other automated image processing techniques. • There is no general theory for determining what is ‘good’ image enhancement when it comes to human perception. If it looks good, it is good!
- 26. References: • Digital image processing by Gonzalez and woods • Digital image processing by S Jayaraman • https://www.slideshare.net/Ayaelshiwi/image-enhancement-29760056 • https://www.techopedia.com/definition/26314/image-enhancement • https://www.mathworks.com/discovery/image-enhancement.html