This document discusses image processing techniques. It defines an image as an artifact that reproduces the likeness of a subject. It discusses two types of image enhancement areas: the spatial domain and frequency domain. Histograms are described as graphs that show the number of pixels for each color intensity value. Common image filters are discussed, including brightness, smoothing, noise reduction, and contrast filters. Operations and applications of image processing like computer vision and medical imaging are also mentioned.

1. IMAGE
PROCESSING
Presented By :-
Lovely Soni
Chartered Institute of Technology,
aburoad.

2. WHAT IS IMAGE ?
Image is an Artifact that reproduce the likeness
of some subjects usually a physical object.

3. IMAGE ENHANCEMENT
 Improving Quality of Image
 Two types of Work Area:
I) Special Domain
II)Frequency Domain
 In Special Domain enhancement achieved by
processing on the actual pixels of image.
 In Frequency Domain it is achieved by processing on a
frequency or intensity.

4. HISTOGRAM
 HISTOGRAM is graph of color intensity value
v/s no. of pixels with that intensity in image.
 HISTOGRAM is Blueprint of image.
 How to construct HISTOGRAM?
 Working with RGB image of 24 bit image ,
HISTOGRAM is prepared for each color .
 Working with Grayscale image of 8 bit
maximum 256 shades ,single HISTOGRAM is
prepared.

5. FILTERS
FILTERS FOR IMAGE PROCESSING:
 Brightness Filter
 Smoothing Filter
 Noise Reduction Filter
 Contrast Filter

6. Brightness Filter
 To increase BRIGHTNESS of image, move its HISTOGRAM
towards high intensity.
 It is achieved by adding some fixed value in each pixels of
image.
 It means HISTOGRAM is shifted by that value and
BRIGHTNESS increased by the same.

7. BRIGHTNESS FILTER
 For eg.
 To increase BRIGHTNESS in RGB image, we have to
add K value in each color that is, RED, GREEN & BLUE.
 Processed pixel= source pixel + Brightness constant

8. Brightness Filter

9. Smoothing Filter
 To apply SMOOTHNESS, mask of 9 pixels is used
 Target pixel
 This mask is applied on all pixels to smooth image.
 To apply this mask we have to move from upper left corner to lower
right corner one by one each pixel.
 The Target pixel is replaced by the MEAN of mask pixels.

10. Smoothing Filter

11. Noise Reduction Filter
 In image any spike (point or itch) comes during scanning called NOISE.
 To remove NOISE, in some methods, mask of 5 pixels of “ + “ sign
shape is used.
 Target pixel
 This mask is applied on all pixels of image.
 Image results in reduced image or low noise image.
 To apply this mask we have to move this mask from upper left corner of
image to lower right corner one by one on each pixel.
 The MEDIAN of mask pixels replaces the Target pixel.

12. Noise Reduction Filter

13. Contrast Filter
 To increase CONTRAST of image, spread up its HISTOGRAM over X-axis.
 It means HISTOGRAM is spread up by a constant value and
CONTRAST increased by the same.
 For eg.
 To increase CONTRAST in Gray image, for each pixels , we
have to subtract mid intensity from pixel intensity then increase or decrease
it by contrast constant and then add mid intensity value to it.
 Processed pixel = (source pixel-128) *(( contrastpercent+100)/100) +128

14. Contrast Filter

15. Operations
• Euclidean geometry transformation
• Color correctness
• Digital compositing
• Image registration
• Image recognition
• etc…

16. Applications
• Computer Vision
• Face Detection
• Feature Detection
• Medical Image Processing
• Remote Sensing
• etc…

17. Thank You….