Presented By:Nisar AhmedWaqas Ahmed

The Application of Image Enhancement on Color and

Grayscale Images

HITEC University TaxilaDepartment of Electrical Engineering

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.

Problem Statement

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.

Basic Working

Application specific image enhancement

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

Image Enhancement Techniques

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

Contrast Enhancement

Results

The images in figure 1 shows the result of color contrast enhancement, the image on the left is a washed out image having poor contrast. After the application of color correction technique which works separately on the RGB layers the resultant image has much better detail and looks like a good image for visual perception.

Images captured in low light are darker and a low contrast. In the figure 2 the image on the left is the original darker image which is first transformed into a lighter image by shifting histogram towards right side and then applying color correction by converting it into intensity image.

Results

Image captured in ambient light having a color other than white may have a color cast. An aerial image may also have a color cast due to low quality LSR or some other reason. One such image is shown in figure 3. This effect can easily be eliminated by applying histogram equalization on intensity layer.

Weiner deconvolution filter provide us best results in debluring when we know the length and angle of distortion in motion blurred images. The results become more and more accurate when we put a value close to the original distortion. Figure 5 shows the result of motion blurred image restored by using correct length and angle of distortion.

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.

Results

Results

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.

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.

Results

Results

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

Results

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%.

Results

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

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.

Conclusion

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.

Recomandation