This is the presentation which was presented at All Pakistan Technical Paper Competition Lahore under the title "The application of image enhancement in color and grayscale images"

1. Presented By:
Nisar Ahmed
Waqas Ahmed
HITEC University Taxila
Department of Electrical Engineering

2.  Image enhancement techniques are used to emphasize
and sharpen image features such as to obtain a visually
more pleasant, more detailed, or less noisy output
image.
 Image enhancement is the process of applying these
techniques to facilitate the development of a solution
to a Digital imaging problem.

3.  The aim of image enhancement is to improve the
interpretability or perception of information in image
for human viewers, or to provide `better' input for
other automated image processing techniques.
Application specific image enhancement

4.  Contrast Enhancement
 Brightness Contrast
 Linear Contrast Stretching
 Histogram Equalization
 Adaptive Contrast Enhancement
 Color Contrast
 Linear Color Contrast
 Color Balance
 Blur Reduction
 Image Sharpening
 Weiner Deconvolution
 Removing Noise
 Linear Filtering
 Median filter
 Adaptive filtering

5.  Histogram of images enhanced by Linear Stretching, Histogram
Equalization and Local Contrast Enhancement.

6. The images in figure 1 shows the Images captured in low light are
result of color contrast darker and a low contrast. In the
enhancement, the image on the figure 2 the image on the left is the
left is a washed out image having original darker image which is first
poor contrast. After the application transformed into a lighter image by
of color correction technique shifting histogram towards right
which works separately on the RGB side and then applying color
layers the resultant image has correction by converting it into
much better detail and looks like a intensity image.
good image for visual perception.

7. Image captured in ambient light Weiner deconvolution filter
having a color other than white provide us best results in debluring
may have a color cast. An aerial when we know the length and
image may also have a color cast angle of distortion in motion
due to low quality LSR or some blurred images. The results
other reason. One such image is become more and more accurate
shown in figure 3. This effect can when we put a value close to the
easily be eliminated by applying original distortion. Figure 5 shows
histogram equalization on the result of motion blurred image
intensity layer. restored by using correct length
and angle of distortion.

8. Cameras having autofocus take a little time to focus the subject so prior capturing of
image may hay some lens blur. If the camera is in high f-stop it is tuned to capture
near object and blur the far objects. If a landscape image is captured in high f-stop
settings the whole image has a Gaussian blur. We can remove this by using Weiner
filter by adjusting its parameters to Gaussian filter which sharpen the image
features. The image in figure 4 is blurred by this effect and has a slightly low
contrast. This image is corrected by sharpening the image followed by histogram
equalization.

9. The Salt and Pepper type noise is typically caused by malfunctioning of the pixel
elements in the camera sensors, faulty memory locations, or timing errors in the
digitization process. For the images corrupted by Salt and Pepper noise, the noisy
pixels can take only the maximum and the minimum values in the dynamic range.
Figure 6 shows an image corrupted by salt and pepper noise. The image is restored
by using median filter. The image on the right side is restored image but its edges
and sharpness is blurred due to median filtering. The blurring can be increased or
decreased by varying the radius of median filter.

10. Sparkle noise causes artificial artifacts in digital images. A bright spot exist in image
which have a typical intensity of 40%. This effect can easily be reduced by using
sigma filter by adjusting the value of threshold. Figure 7 shows the image corrupted
by sparkle noise and then restored by adjusting a suitable value of sigma filter. This
image shows that sigma filter produce much better results than median filter in
some cases.

11. Histogram equalization shows best result in most of the cases but if the
image has a wide light color area we will incorporate adaptive histogram equalization.

12. Sigma filter is best among all the discussed techniques because it better
preserve the edges while removing the noise, we can use it as a median
filter by adjusting the value of threshold to 100%.

13. In image sharpening and motion blur reduction only two
techniques are discussed which produces best results in their
application area.

14.  Image enhancement techniques are widely
available, but their applications are not well defined.
Application software has been designed to check the
effect on the filter before its application. A detailed
discussion has been made on the base of results to
select an algorithm on the base of filtering
requirements. These techniques are tested on a large
number of images and have shown significant results.

15. various aspects of image enhancement are catered for
in the implementation and subsequent exercise of
results, nevertheless, we understand that it is so
demanding and absorbing area for research that the
work could substantially be carried forward in
following directions as a future work:
 Improvement in selective noise reduction techniques.
 Level correction of image by combining it with image
segmentation.
 Noise reduction by anisotropic diffusion using closed edges.