image enhancement image enhancement imag

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