In today’s era , Images are very indispensable part of almost every field. But there is a difference between the human level of perception and digital computer systems. To fill this gap image processing is used. Images can be affected by the sensor characteristics, atmospheric influences, external noise etc.; these factors affect the human perception of the image. So to cope up with these problems image enhancement is applied. Major problem come with images is the poor contrast of the image due to bad weather, poor lightening conditions while image acquisition or the shutter speed of the camera. So to improve the contrast of the image. A high contrast image improves the user’s perception and visualization. So image enhancement is required at this point.
An image is an array, or a matrix, of square pixels arranged in rows and columns. An image is a 2-Dd function f(x, y), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. Image Processing mainly consists of methods to convert an image into digital image form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. There are various Image Processing Techniques like Image Enhancement, Image Analysis, Image Restoration, Image Representation, Image Segmentation etc.
The main objective of Image Enhancement is to process an image so that the output image will be better than the original input image. So this technique enhances and improves the quality of the image. Image enhancement techniques can be divided into two broad categories:
1. Spatial Domain, which operates directly on pixels an manipulate them. Spatial domain processes will be denoted by the expression
g (x, y)=Tf(x, y)
Where f(x, y) is the input image, g(x, y) is the processed image and T is an operator on f defined on the neighborhood of (x, y).
2. Frequency Domain, operates on the Fourier transform of the image .In frequency domain, enhancement is mainly based on DFT. At first, DFT is applied on the original image then some suitable filters are applied. At last inverse DFT is applied to get the enhanced image. The basic filtering equation for an image f(x, y) of size m*n is given as:
g (x, y)=T-1 H(u, v) F(u,v)
Where T-1 is the inverse DFT (Distributed Frequency Transformation). H (u, v) is the filter function. F (u, v) is the DFT original image f(x, y) and g(x, y) is the processed image.
Image enhancement plays an important role in the application areas of Geographic Information System, Industrial Inspection, Digital Photography, Medical Image Processing etc.