Enhancement Methods in Image Processing

Enhancement of an image simply means to convert or process an original image to other desired form which is suitable for user application. Availability of enormous enhancement methods sometimes lead to ambiguity in choosing an algorithm to process an image.
In this article, I will mention in brief about the uses of different methods which can be chosen at appropriate situations for beginners who have just entered the field of image processing. There are two important methods used for enhancement. Spatial Domain (direct manipulation of pixels of the image) and Frequency Domain (modifying the Fourier Transform of an image).

Spatial domain is directly applied on the pixel and is defined by g(x,y)=T[f(x,y)].If the processing is done over a 1*1 neighborhood, it is termed as point processing. Point operation include contrast stretching, noise clipping, window slicing and histogram modeling.

Spatial operations include noise smoothing, median filtering, lp, hp & bp filtering and zooming. There are other available methods such as transform operation and pseudo-coloring too.
On learning more about contrast stretching, the idea behind contrast stretching is to increase the dynamic range of the gray levels in the image that is being processed. Sometimes the dynamic range of a processed image far exceeds the capability of the display device, in which case only the brightest parts of the images are visible on the display screen at these situations we can use compression of the dynamic range. Gray level slicing is used for highlighting a specific range of gray levels in an image often is desired. Applications include enhancing features such as masses of water in satellite imagery and enhancing flaws in x-ray images.

Histogram processing can be done in three ways:
• histogram equalization,
• histogram specification and
• local enhancement.

The use of spatial masks for image processing is called spatial filtering. Smoothing filters are used for blurring and for noise reduction. To highlight fine detail in an image or to enhance detail that has been blurred, either in error or as a natural effect of a particular method of image acquisition. Uses of image sharpening vary and include applications ranging from electronic printing and medical imaging to industrial inspection and autonomous target detection in smart weapons. here in this we use masks such as prewitt, sobel or roberts.


Enhancement in the frequency domain
We simply compute the Fourier transform of the image to be enhanced, multiply the result by a filter transfer function, and take the inverse transform to produce the enhanced image.
Spatial domain: g(x,y)=f(x,y)*h(x,y) ¬ Frequency domain: G(w1,w2)=F(w1,w2)H(w1,w2).
Image Enhancement is one of the most necessary and important techniques in image research. The aim of enhancement is to improve the visual appearance of an image, or to provide a better transform representation for future automated image processing. Many images like medical images, satellite images, aerial images and even real life photographs suffer from poor contrast and noise. It is necessary to enhance the contrast and remove the noise to increase image quality. One of the most important stages in medical images detection and analysis is Image Enhancement techniques which improves the quality (clarity) of images for human viewing, removing blurring and noise, increasing contrast, and revealing details are examples of enhancement operations. The enhancement technique differs from one field to another according to its objective.

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Architha Reddy is pursuing M.Tech in NITTE, Bangalore. Apart from studies she also researches on different topics in Computer Science and contributes write-ups.

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March 23, 2020
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