The description of MatLab processes Essay

When user open the MATLAB then the following starting screen will appear. The MATLAB has number of components associated with it. The command window will appear at first place which is used in order to specify the operation on the image. The clarity within the image will be introduced when filtering mechanism is applied. The median filter is inbuilt in MATLAB. The command window has following structure associated with it. The noise can be introduced within the image by the use of imnoise function present within the MATLAB. The MATLAB is software which has tools of varying types. The user either can operate using command user interface or by the use of graphical user interface. The user can select type of operation from the file menu. The MATLAB provides the mechanism in which Graphical user interface can be used in order to perform operation either by clicking and double clicking. The command user interface can be used only by expert users. These users know the command associated with MATLAB. The Graphical user interface can be utilized by naive users also. The users of the MATLAB are comfortable in using MATLAB since abstraction is provided by the proposed system. The abstraction is the mechanism which provides separation of design and coding. The separation will help in the designing process.
The detailed examination of low contrast digital image is a challenging issue. Low contrast makes it difficult for viewer to bring out the detailed features of the image. Histogram Equalisation (HE) is an efficient way to intensify the contrast of images. However, traditional HE techniques generally result in immoderate intensification. Many methods are proposed on the basis of traditional HE to enhance the contrast of images. In this paper, we survey the various techniques of HE specifying their contribution to enhance the contrast of digital image and their results are compared.
In Histogram Equalization an image is considered as a two-dimensional array of gray levels. Let the (a, b) elements of this array is X (a, b) be the intensity of (a, b) pixel of the image, where X(a, b) is from the L discrete gray levels denoted by {X0, X1, . . . . , XL-1}. The probability density function of grey level Xk denoted by P[Xk], P[Xk]= nk/n , k = 0, 1, . , L-1 where nk is the number of pixel with intensity k and n is the entire pixels in image. C[Xk ] denotes the cumulative distribution function (CDF) of Xk and defined as C[Xk]=P[Xx] = _(b=0)^kn/nk , k= 0,1,. . , L-1 and C [XL-1] = 1. Now we define a transformation function for HE, which maps an input gray level Xk into an output gray level F (k), given as
F (Xk) = X0 + (XL-1- X0) C [Xk], k=0, 1,.., L-1.
Absolute mean brightness error (AMBE) It is used to measure brightness conservation in the processed image. AMBE defined as AMBE (I, J) = | IM - JM |. where IM is the