Image DEF: The term image refers to a 2-D light intensity function f (x, y), where (x, y) denote spatial coordinates and the values of intensity function,’f’ at a point (x,y) are proportional to the brightness (gray level) of the image at that point. It is represented in matrix form where row and column indices identify a point in image and the corresponding matrix element identifies the brightness of the image at that point. These elements of the matrix are called as PIXELS or Picture Elements or Image Elements. The term digital image processing generally refers to the processing of 2-D data in an array of real or complex numbers represented by a finite number of bits (binary digits). Image Enhancement Objective :- To process an image so that the result is more suitable than the original image for a specific application. There are several techniques used for enhancement of images. Some of the common image enhancement techniques are :- Techniques used for Enhancement POINT OPERATIONS: - Contrast Stretching Clipping and Thresholding. Image Negatives Image Subtraction and Change Detection:- SPATIAL OPERATIONS:- Smoothing Filters Median Filtering Sharpening Filters Image Restoration Any image electronic means is likely to be degraded by the sensing environment. The degradations many be in the form of sensor noise, blur due to camera misfocus, relative object camera motion, random atmospheric turbulence and so on. Image Compression The term “data compression” refers to the process of reducing the amount of data required to represent a given quantity of information. In digital image compression, three basic data redundancies can be identified. They are: 1. Coding redundancy. 2. Inter pixel redundancy. 3. Psycho visual redundancy. FIDELITY CRITERION 1. Objective Fidelity Criteria 2. Subjective Fidelity Criteria Image Compression Models A compression system consists of two distinct structural blocks, which are shown below: 1) Encoder. 2) Decoder. F(x,y) source channel channel source Encoder encoder channel decoder decoder -f’(x,y) Data Compression strategies: 1. Lossless Compression 2. Variable –length coding Example: Original data stream: 17 8 54 0 0 0 97 5 16 0 45 23 0 0 0 0 0 3 67 0 0 8 .. Run length Encoded data: 17 8 54 03 97 5 16 0 1 45 23 05 3 67 02 8 .. 3. Huffman coding 4. Loosy compression Image Analysis IMAGE SEGMENTATION 1. The methods for segmentation are generally based on one of the two basic properties of the gray – level values. 2. Discontinuity 3. Similarity APPLICATIONS:- 1. Develops machine that could perform visual functions of living beings. 2. Space image applications 3. In archeology, physics and related fields high energy plasmas and electron microscopy. 4. Radar and sonar images. CONCLUSIONS:- They motivate, captivate, educate and inform more than words ever can.