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.

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.

4. Image enhancement techniques:

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