This paper mainly deals with digital image processing, different stages in image processing and its profound applications in the present era. This also explains its applications found in medical, military, Robotics fields. This paper stresses the importance and its impact in the future.

The main feature Digital Image Editing used for altering and improving images in an all most endless number of time. The other features of this technology are Image Size Alteration, Cropping on Image, Removal of Noise and unwanted elements, Image Compression, merging of images and finally color adjustments and finally advantages and disadvantages of digital image processing.

Digital Image Processing is concerned with acquiring and processing of an image. In simple words an image is a representation of a real scene, either in black and white or in color, and either in print form or in a digital form i.e., technically an image is a two-dimensional light intensity function. In other words it is a data intensity values arranged in a two-dimensional form like an array, the required property of an image can be extracted from processing an image. Image is typically by stochastic models. It is represented by AR model. Degradation is represented by MA model.

 Other form is orthogonal series expansion. Image processing system is typically non-casual system. Image processing is two dimensional signal processing. Due to linearity Property, we can operate on rows and columns separately. Image processing is vastly being implemented by “Vision Systems” in robotics. Robots are designed, and meant to be controlled by a computer or similar devices. While “Vision Systems” are most sophisticated sensors used in Robotics. They relate the function of a robot to its environment as all other sensors do. “Vision Systems” may be used for a variety of applications, including manufacturing, navigation and surveillance. Some of the applications of Image Processing are:

        1. Robotics. 3. Graphics and Animations.

        2. Medical Field. 4. Satellite Imaging

DIGITAL IMAGE PROCESSING:

Digital image processing is the use of computer to perform on. Digital image processing has the same advantages (over analog image processing) as has (over analog signal processing) -- it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing

1. IMAGE ACQUISITION:

An image is captured by a sensor (such as a monochrome or color TV camera) and digitized. If the output of the camera or sensor is not already in digital form, an analog-to digital converter digitizes it.

2. RECOGNITION AND INTERPRETATION:

Recognition is the process that assigns a label to an object based on the information provided by its descriptors. Interpretation is assigning meaning to an ensemble of recognized objects.

3. SEGMENTATION:

Segmentation is the generic name for a number of different techniques that divide the image into segments of its constituents. The purpose of segmentation is to separate the information contained in the image into smaller entities that can be used for other purposes.

4. REPRESENTATION AND DESCRIPTION:

Representation and Description transforms raw data into a form suitable for the Recognition processing.

5. KNOWLEDGE BASE:

A problem domain detailing the regions of an image where the information of interest is known to be located is known as knowledge base. It helps to limit the search.

THRESHOLDING:

Threshold is the process of dividing an image into different portions by picking a certain grayness level as a threshold, comparing each pixel value with the threshold, and then assigning the pixel to the different portions, depending on whether the pixel’s grayness level is below the threshold or above the threshold value. Threshold can be performed either at a single level or at multiple levels, in which the image is processed by dividing it into ” layers”, each with a selected threshold. Various techniques are available to choose an appropriate threshold ranging from simple routines for binary images to sophisticated techniques for complicated images. 

CONNECTIVITY:

Sometimes we need to decide whether neighboring pixels are somehow “connected” or related to each other. Connectivity establishes whether they have the same property, such as being of the same region, coming from the same object, having a similar texture, etc. To establish the connectivity of neighboring pixels, we first have to decide upon a connectivity path.

NOISE REDUCTION:

Like other signal processing mediums, Vision Systems contains noises. Some noises are systematic and come from dirty lenses, faulty electronic components, bad memory chips and low resolution. Others are random and are caused by environmental effects or bad lighting. The net effect is a corrupted image that needs to be preprocessed to reduce or eliminate the noise. In addition, sometimes images are not of good quality, due to both hardware and software inadequacies; thus, they have to be enhanced and improved before other analysis can be performed on them.

CONVOLUTION MASKS:

A mask may be used for many different purposes, including filtering operations and noise reduction. Noise and Edges produces higher frequencies in the spectrum of a signal. It is possible to create masks that behave like a low pass filter, such that higher frequencies of an image are attenuated while the lower frequencies are not changed very much. There by the noise is reduced.

EDGE DETECTION:

Edge Detection is a general name for a class of routines and techniques that operate on an image and results in a line drawing of the image. The lines represented changes in values such as cross sections of planes, intersections of planes, textures, lines, and colors, as well as differences in shading and textures. Some techniques are mathematically oriented, some are heuristic, and some are descriptive. All generally operate on the differences between the gray levels of pixels or groups of pixels through masks or thresholds. The final result is a line drawing or similar representation that requires much less memory to be stored, is much simpler to be processed, and saves in computation and storage costs. Edge detection is also necessary in subsequent process, such as segmentation and object recognition.

IMAGE DATA COMPRESSION:

Electronic images contain large amounts of information and thus require data transmission lines with large bandwidth capacity. The requirements for the temporal and spatial resolution of an image, the number of images per second, and the number of gray levels are determined by the required quality of the image. Recent data transmission and storage techniques have significantly improved image transmission capabilities, including transmission over the Internet.